From 42e6dfa9be08fdf2b82127236c7cdee08f017efe Mon Sep 17 00:00:00 2001 From: nfrerebeau Date: Fri, 26 Jul 2024 12:55:10 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20tesselle?= =?UTF-8?q?/packages@cbf3ea48a1548755e256d02053daa3cf1961a58f=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- aion/dev/pkgdown.yml | 2 +- alkahest/pkgdown.yml | 2 +- ananke/pkgdown.yml | 2 +- arkhe/dev/pkgdown.yml | 2 +- dimensio/dev/pkgdown.yml | 2 +- folio/dev/pkgdown.yml | 2 +- isopleuros/dev/pkgdown.yml | 2 +- kairos/dev/pkgdown.yml | 2 +- khroma/dev/pkgdown.yml | 2 +- kinesis/pkgdown.yml | 2 +- nexus/pkgdown.yml | 2 +- sitemap.xml | 2 +- tabula/dev/articles/alpha.html | 4 ++++ tabula/dev/articles/beta.html | 4 ++++ tabula/dev/news/index.html | 4 ++++ tabula/dev/pkgdown.yml | 2 +- tabula/dev/reference/Rplot004.png | Bin 37458 -> 37527 bytes tabula/dev/reference/color.html | 8 ++++++++ tabula/dev/reference/occurrence-1.png | Bin 317531 -> 317813 bytes .../reference/palette_color_continuous.html | 8 ++++++++ .../dev/reference/palette_color_discrete.html | 8 ++++++++ .../dev/reference/palette_color_picker.html | 8 ++++++++ tabula/dev/reference/plot_heatmap-1.png | Bin 17715 -> 17630 bytes tabula/dev/reference/plot_heatmap-2.png | Bin 19151 -> 19121 bytes tabula/dev/reference/plot_heatmap-3.png | Bin 19673 -> 19642 bytes tabula/dev/reference/plot_heatmap.html | 6 +++--- tabula/dev/reference/plot_rank-1.png | Bin 71772 -> 75243 bytes tabula/dev/reference/plot_spot-1.png | Bin 95658 -> 96761 bytes tabula/dev/reference/plot_spot-2.png | Bin 47618 -> 48187 bytes tabula/dev/reference/plot_spot-3.png | Bin 94252 -> 95082 bytes tabula/dev/reference/plot_spot-4.png | Bin 88817 -> 89006 bytes tabula/dev/reference/plot_spot.html | 6 +++--- tabula/dev/reference/profiles-1.png | Bin 48913 -> 51672 bytes tabula/dev/reference/reexports.html | 12 ++++++++++++ tabula/dev/reference/similarity-1.png | Bin 107803 -> 108196 bytes tabula/dev/search.json | 2 +- tekton/pkgdown.yml | 2 +- tesselle/pkgdown.yml | 2 +- 38 files changed, 78 insertions(+), 22 deletions(-) create mode 100644 tabula/dev/reference/color.html create mode 100644 tabula/dev/reference/palette_color_continuous.html create mode 100644 tabula/dev/reference/palette_color_discrete.html create mode 100644 tabula/dev/reference/palette_color_picker.html diff --git a/aion/dev/pkgdown.yml b/aion/dev/pkgdown.yml index 9811674ba..b85552e77 100644 --- a/aion/dev/pkgdown.yml +++ b/aion/dev/pkgdown.yml @@ -5,7 +5,7 @@ articles: aion: aion.html bibliography: bibliography.html extending: extending.html -last_built: 2024-07-26T12:31Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/aion/reference article: https://packages.tesselle.org/aion/articles diff --git a/alkahest/pkgdown.yml b/alkahest/pkgdown.yml index aa6f2138c..4851c5a4a 100644 --- a/alkahest/pkgdown.yml +++ b/alkahest/pkgdown.yml @@ -4,7 +4,7 @@ pkgdown_sha: ~ articles: baseline: baseline.html smoothing: smoothing.html -last_built: 2024-07-26T12:31Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/alkahest/reference article: https://packages.tesselle.org/alkahest/articles diff --git a/ananke/pkgdown.yml b/ananke/pkgdown.yml index faf71b5c7..8f6be89b5 100644 --- a/ananke/pkgdown.yml +++ b/ananke/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.1.0 pkgdown_sha: ~ articles: radiocarbon: radiocarbon.html -last_built: 2024-07-26T12:31Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/ananke/reference article: https://packages.tesselle.org/ananke/articles diff --git a/arkhe/dev/pkgdown.yml b/arkhe/dev/pkgdown.yml index fd052bbcd..a9ade41a7 100644 --- a/arkhe/dev/pkgdown.yml +++ b/arkhe/dev/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.0 pkgdown_sha: ~ articles: {} -last_built: 2024-07-26T12:31Z +last_built: 2024-07-26T12:50Z urls: reference: https://packages.tesselle.org/arkhe/reference article: https://packages.tesselle.org/arkhe/articles diff --git a/dimensio/dev/pkgdown.yml b/dimensio/dev/pkgdown.yml index 019a567ca..26c1796a5 100644 --- a/dimensio/dev/pkgdown.yml +++ b/dimensio/dev/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.1.0 pkgdown_sha: ~ articles: pca: pca.html -last_built: 2024-07-26T12:32Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/dimensio/reference article: https://packages.tesselle.org/dimensio/articles diff --git a/folio/dev/pkgdown.yml b/folio/dev/pkgdown.yml index 601b68ecb..ef71d61bb 100644 --- a/folio/dev/pkgdown.yml +++ b/folio/dev/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.0 pkgdown_sha: ~ articles: {} -last_built: 2024-07-26T12:32Z +last_built: 2024-07-26T12:50Z urls: reference: https://packages.tesselle.org/folio/reference article: https://packages.tesselle.org/folio/articles diff --git a/isopleuros/dev/pkgdown.yml b/isopleuros/dev/pkgdown.yml index aee1296cc..0122d82d7 100644 --- a/isopleuros/dev/pkgdown.yml +++ b/isopleuros/dev/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.0 pkgdown_sha: ~ articles: {} -last_built: 2024-07-26T12:32Z +last_built: 2024-07-26T12:50Z urls: reference: https://packages.tesselle.org/isopleuros/reference article: https://packages.tesselle.org/isopleuros/articles diff --git a/kairos/dev/pkgdown.yml b/kairos/dev/pkgdown.yml index 76a87b04e..e0850690b 100644 --- a/kairos/dev/pkgdown.yml +++ b/kairos/dev/pkgdown.yml @@ -4,7 +4,7 @@ pkgdown_sha: ~ articles: event: event.html seriation: seriation.html -last_built: 2024-07-26T12:32Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/kairos/reference article: https://packages.tesselle.org/kairos/articles diff --git a/khroma/dev/pkgdown.yml b/khroma/dev/pkgdown.yml index 6fbcdbc66..f168f71e5 100644 --- a/khroma/dev/pkgdown.yml +++ b/khroma/dev/pkgdown.yml @@ -4,7 +4,7 @@ pkgdown_sha: ~ articles: crameri: crameri.html tol: tol.html -last_built: 2024-07-26T12:32Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/khroma/reference article: https://packages.tesselle.org/khroma/articles diff --git a/kinesis/pkgdown.yml b/kinesis/pkgdown.yml index 41d7c2858..2520e5e0d 100644 --- a/kinesis/pkgdown.yml +++ b/kinesis/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.0 pkgdown_sha: ~ articles: {} -last_built: 2024-07-26T12:32Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/kinesis/reference article: https://packages.tesselle.org/kinesis/articles diff --git a/nexus/pkgdown.yml b/nexus/pkgdown.yml index d457b2cab..90bf18e7e 100644 --- a/nexus/pkgdown.yml +++ b/nexus/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.1.0 pkgdown_sha: ~ articles: nexus: nexus.html -last_built: 2024-07-26T12:32Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/nexus/reference article: https://packages.tesselle.org/nexus/articles diff --git a/sitemap.xml b/sitemap.xml index 25f9c80c1..dbcf9295d 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,6 +2,6 @@ https://www.tesselle.org/index.html - 2024-07-26T12:34:27.540Z + 2024-07-26T12:53:32.552Z diff --git a/tabula/dev/articles/alpha.html b/tabula/dev/articles/alpha.html index b94bd88e9..55dc51155 100644 --- a/tabula/dev/articles/alpha.html +++ b/tabula/dev/articles/alpha.html @@ -125,6 +125,10 @@

2024-07-26

## Load package library(tabula) +#> Warning: replacing previous import 'arkhe::palette_color_continuous' by +#> 'khroma::palette_color_continuous' when loading 'tabula' +#> Warning: replacing previous import 'arkhe::palette_color_discrete' by +#> 'khroma::palette_color_discrete' when loading 'tabula' ## Ceramic data from Lipo et al. 2015 data("mississippi", package = "folio") diff --git a/tabula/dev/articles/beta.html b/tabula/dev/articles/beta.html index 062173fe9..b1dc091fc 100644 --- a/tabula/dev/articles/beta.html +++ b/tabula/dev/articles/beta.html @@ -95,6 +95,10 @@

2024-07-26

## Load packages library(tabula) +#> Warning: replacing previous import 'arkhe::palette_color_continuous' by +#> 'khroma::palette_color_continuous' when loading 'tabula' +#> Warning: replacing previous import 'arkhe::palette_color_discrete' by +#> 'khroma::palette_color_discrete' when loading 'tabula' ## Ceramic data from Lipo et al. 2015 data("mississippi", package = "folio") diff --git a/tabula/dev/news/index.html b/tabula/dev/news/index.html index 77b33e2d9..539d8ef41 100644 --- a/tabula/dev/news/index.html +++ b/tabula/dev/news/index.html @@ -44,6 +44,10 @@

tabula 3.1.0.9000

+
+

Internals

+
  • Use palette functions from khroma.
    • +

tabula 3.1.0

CRAN release: 2024-05-16

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    Usage # S4 method for class 'matrix' plot_heatmap( object, - col = grDevices::hcl.colors(12, "YlOrBr", rev = TRUE), + col = color("YlOrBr")(12), diag = TRUE, upper = TRUE, lower = TRUE, @@ -69,7 +69,7 @@

    Usage # S4 method for class 'data.frame' plot_heatmap( object, - col = grDevices::hcl.colors(12, "YlOrBr", rev = TRUE), + col = color("YlOrBr")(12), diag = TRUE, upper = TRUE, lower = TRUE, @@ -84,7 +84,7 @@

    Usage # S4 method for class 'dist' plot_heatmap( object, - col = grDevices::hcl.colors(12, "YlOrBr", rev = TRUE), + col = color("YlOrBr")(12), diag = FALSE, upper = FALSE, lower = !upper, diff --git a/tabula/dev/reference/plot_rank-1.png b/tabula/dev/reference/plot_rank-1.png index e0a5f29f10efc49527836f96dd5c3c45b35beb46..a9d1cfc04cf07e1cee957363bfff6f1a661f8f68 100644 GIT binary patch literal 75243 zcmeFYg;$er{5QVQAs_;xbV`?`v?4J=baa=9bTm)t&b{k?U$5)k@7Eps_>m?R1rr4T0H6YCJp=;)#6bW6p*`ELLQ*h{RwYwA`cw{%p7t!h5+UDVJ;%^ifp)zWqpI0V-ifr65v z?Ih&^rT*{he-Zd!1pXI+|NkHW`^hOK4@gi@AoL_Dk&?gu&j(7+sYH$vGBdM&eLgw6 z29Vlmza11&^dk8$F5q!fLd&T&4h4^j1Bsfq?EBXf@KaGnBBs(QS2eYD5;Q7U6#_5qvl^POdIFc8|q z<-K`Bk%dsm!eTLXFtKHCHz83UFUMGOV1UYK9(6keMwr4QolkBpP`7rW?oP8sKPnKj zQ}??5e(N0pf``^WCmBX^+U$RSyyIbc`n#AiGeB0#^lGb1lQQtZx_xyx7H$X6g`TFG z3qD8R!Jh?4@H9?S{#gLNVm$cqaW+JZ*Z5)v!Fwad$5x%Q-NiAyuOyMQV#4ljzP-*J-q}<1u=68#{q*Zh|gk4B5CBH{k4LZL63OC#rk(J zf#yMd#P|zp-HVpoZUBQKWUMJK$kDfdyDD6d1yYpvF6R*od`T&Ce{pU)|H^sToMbt{z3^6z2kL_-f{ z{3)(LWnv5nTT0#Lnx7_G42=X`>N<1Qi1+U-Y=_Lob!f1;ANs=LgCw?)2yR)YGZirpgWA|0R# zpIaoM`t+~Yi{X|i4-_xV_X)ur!hxp}Y1=q^l!a)pOGN?PrYMjfDQ@f_5}Og_SL%CcBp zm1>-RfJ)v(nO*b2puR~R{KyQ(Rv*E^tEf;1$pC@Y%OfGs+|Oqp)5<)BT5=|$erLhc zW|U=tDtCB;LxsQY&;Uk=u_>%=%*`3;96KN~_P)$Z@0g`6J9Q}>b}|k@|KhvFOu}Hs*l2$tQ~MzfKt*EuEq=pMSC*Fs@>K@fkCWf zW>s@FwcVwG&b21oWp}WwQU1LT%IWhe)D>H+$3i|!fxzhrr4NOhfRXpI{c4dmM!!~! z;446DjC84XfcXiU1swVU>&A85wQVMcafao$_Z)|S?VD~!`-Jl(INS*;zFx)KFKgIg zCMM|2uyeMF``lhU<4}&U9 z_Hv)|V`6{Y1XuqoBSt$`SQS6*;+peWSX^SOT9qhE_jK~!=Q!-t@+?3S!xvlCrJ7Y; zQoMvqsk)V6*3D=2;hm0u@)=G7l?a;@*HUo7^A>g<*i$F)s}}W&I}PUiWmEsD^b&Hc zs0;hGogrSC-r>Ujr~8A~PPnb*EVoaJ26%~>tafa|x}88u4>tLu^B!3R@a-K5%FaD} zes8C<&o{q9(@@WaU~kH;ox)V%>zqn*$d?^0mvt{-23=7@BW0Xakn zYA%rb8FR!zw|`_NIHjG}QTriuwRZ@%C=^{Ea_gC>V`Dc`vmEY6h)AAGT8}2d*Jwn-Vggt$ITRoIt-<>ff@cLegU9U7#a5>o@)_d@>{Dn6}mPrNdM*^ zVAi?g;UXCzqoXWu9W-YKy$W|Qato!YiB8XkQ1AJHN2gt^D1|wU3b1_Zt99OyEws%y zvUSXLw^rdfa2qXmIrGRos6ix-?SXJn-l`^e-t>!*(T#5ZnYAD4t71%B7RFZym1P93 z^xNF(J}DSUV5F9F^*WQ*>b#~1(l!iu+N6US!aer5c!276vnFcQs+0>!&0=S<*DW%u zD=45cQjN4nsv@1H3qKyne$LC@Lh7~1@1ifauRM5 zsRG#4%4i4#4r;mIK456uwS8`~XHATEUuTV5Q-KAtDT0rMa&+P#;+(R|c(c%aksIkt zIV9v0zG3&iz*Vw26Df96K2lT5XjGl`rxS+S776gpJyX=Wy!%Cnf!2T>=@a-(h6=x} z)D8Fh9vAMm1~NzzQ*D>&Jyz5;FlF>QoChov-2&GHi2&s5wo8%)HOh@htdqBW1q7Te zt7?YT@xgTNfG zLmE9D2E}M$Pli3YVFVAzgt+(pUk1sokNwU2%CaawTOi3{Jg(A4@k$~qNUDnuqpoxm zJ4L$Ma#f!en_WR7BqXkqHHly7+*n)oZ75j||7;oejta3b3l#U?BboZY$?KSw!tLfaB57@mDar+IYJy0?lpC!G z04Z=6xWhanUQE0HVk;ad-T=R7Y1tnmIOI!C-NcyO&YBQ6-qR-5oon|vUI9r?6SlGY zF5gzL_TMtu_4K=Py*e-hqN~=BzmzjNT+eyV!MY;{rn}+O<@2Y7;)-ia;y{ zVYlAuThBk(P?)vxX;N`Sxt!}qZr|UvH*mW7Yg)+Xu)xF?D+A93#dj*HniF|Yc_}-8 zDTjuaSU3)>4Jifk>sxV@6E^!rLWoe;!CZS9_@wdrp2Jw^e2MCXngkG?v?Z5Bn3Rgd zF@uy+W9&bz)>_YMK?)YGh0&lXOY?e3P@`s+VimrYO@OYU{@rr%z(CtBw~Ll2%!2Cq zWvh4OG~W-5LXg?c*$e1s+D+#LEf*0V(VD<7OENtzll+dv)rO{)#J?RMQj>~$`bk}Z z{CpZaXww*A?Y(Nk|4N;(g8z66kmbkXwR&;FTW&MnX^Z32km-&_P|7Kuq92AZS~6Fu zdL|*c$cyrCUeBmc4cWy8s#I_1&EEu06Aq-NW;e`mAX*)!d2RDE`otMulkQ+TJX7#+`PLZw#N0QA<2T8Ni>w zfiS^SXy;LoTEGJEf+ENmdU-0`En_-Tv3gqjQRzD|JokKPMyM?hRK|r3diCEPr)&iI z`)epTW@D~OtFqX{fqd*eaJT(qPVuH9|AWAOJKo(*pCuatSy{p4D05JJ%W+ov)@BQg zVqn`P=wcf*nWnB>2cb5#ELz&pC~IV?VCWaNl`h?AOw}kDFb|Shza05Aj*S5NIW6IC z**>BaNTG4AJsJV7Pz}(78{8NEc-X80aOOo0Q zb$Gc_&-bB&9x8X~U8w*t9?w-^;5*CF?6*J2TlRyg;y*O&6Nr87PXbax^Ry-tdBJzx zWcDy_BYt2XVNb%+R=KYCbGvn`=Sb_6T)If1$efua50n%*{zd-A5(LW7;(jw#kTZrG zpu%O+_w9qn>^*5x1sj}dmUj_lb934fQiFJvdKzUSxb}T% zBvh)z(~&PgH`{2qSo-h&UPA+oJD!m3R;lq1mpyFdX&cZ>pn$#qIBp>lta*aogkQq? 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    Usage plot_spot( object, type = c("ring", "plain"), - col = grDevices::hcl.colors(12, "YlOrBr", rev = TRUE), + col = color("YlOrBr")(12), diag = TRUE, upper = TRUE, lower = TRUE, @@ -70,7 +70,7 @@

    Usage plot_spot( object, type = c("ring", "plain"), - col = grDevices::hcl.colors(12, "YlOrBr", rev = TRUE), + col = color("YlOrBr")(12), diag = TRUE, upper = TRUE, lower = TRUE, @@ -85,7 +85,7 @@

    Usage plot_spot( object, type = c("ring", "plain"), - col = grDevices::hcl.colors(12, "YlOrBr", rev = TRUE), + col = color("YlOrBr")(12), diag = FALSE, upper = FALSE, lower = !upper, diff --git a/tabula/dev/reference/profiles-1.png b/tabula/dev/reference/profiles-1.png index 85037b597961274648be6632dda47ddfe44dccb9..6da89c2114533725de5e2d7e64e4fdb0f5bde9e1 100644 GIT binary patch literal 51672 zcmeFZ`9GBH`v*+Ir-Zw^?j&1x4U$k~-|9{@*^2CI*~-2T21D+mY?-lSFIlq7K6WXL zWn`zZOxd!{U@!(Vo^z(p=Xw5s=hyGNUJ|eCIVcjX=gA8vnV6V3 zq4zWln3z}tn3!0e{D%$v2ABMP1AJq9qOGL?zJm|tP9p+*JK=fH%!i3dAd>#Y9KW#X zz{GTs3951XQ9#DpG|bn*DMS)aMpet+)tdXH%g&BNT0UXFE)ljY44KmAlo7H0=fuC~ z+{12NY}366`QzdV;S-R5{oBvtnEC$$LHkDO%H&_*5ts~XKg$R289XZx+j0>3rACKI zzTY)z$;_pkvb(8fK~x_r2^@fhDl;=y_J9BUp9lWW1ONX$5SH}5S|Chz=^QjG!QKvc zn{M&xv;)){We>yMHZFQ!%TlqZav8&cO`yJJHiNKBuF8az( zzpZ+89#R+Y5j1=8r1kf$!ak2ycJNzUw}v~59Mwd|dfjGiIWAULk40PL8UjEmrq$qb4I zQXU!y_4X}(RgXQ}BtBbpuJov(<}Ce+lABU{ish<1VFh@$hZ|+C?81Q~pZ7(fE#ID; 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    Skip to contents @@ -64,6 +72,10 @@

    remove_NA, remove_constant, remove_zero, replace_NA, replace_zero, sparsity

    +
    khroma
    +

    color, palette_color_continuous, palette_color_discrete, palette_color_picker

    + +

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-[{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"shannon-wiener-diversity-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Information theory index","what":"Shannon-Wiener diversity index","title":"alpha Diversity","text":"Shannon-Wiener index (Shannon 1948) assumes individuals randomly sampled infinite population taxa represented sample (reflect sample size). main source error arises failure include taxa sample: error increases proportion species discovered sample declines (Peet 1974; Magurran 1988). Heterogeneity finite sample: H=−∑=1Sp̂ilnp̂H = - \\sum_{= 1}^{S} \\hat{p}_i \\ln \\hat{p}_i used maximum likelihood estimator (MLE) known negatively biased sample size (error rarely significant; Peet 1974). bias correction (unbiased TRUE): H=−∑=1Sp̂ilnp̂+S−12n H = - \\sum_{= 1}^{S} \\hat{p}_i \\ln \\hat{p}_i + \\frac{S - 1}{2n} Evenness: E=HlnS=−∑=1Sp̂ilogSp̂E = \\frac{H}{\\ln S} = - \\sum_{= 1}^{S} \\hat{p}_i \\log_S \\hat{p}_i","code":"index_shannon(woodland) #> [1] 2.407983 index_shannon(woodland, unbiased = TRUE) #> [1] 2.463865 index_shannon(woodland, evenness = TRUE) #> [1] 0.8038044"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"brillouin-diversity-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Information theory index","what":"Brillouin diversity index","title":"alpha Diversity","text":"Brillouin index (Brillouin 1956) describes known collection: assume random sampling infinite population. Pielou (1975) Laxton (1978) argue use Brillouin index circumstances, especially preference Shannon index. Heterogeneity: H=ln(n!)−∑=1Sln(ni!)n H = \\frac{\\ln (n!) - \\sum_{= 1}^{S} \\ln (n_i!)}{n} Evenness: E=HHmax E = \\frac{H}{H_{max}} : Hmax=1nlnn!(⌊nS⌋!)S−r[(⌊nS⌋+1)!]r H_{max} = \\frac{1}{n} \\ln \\frac{n!}{\\left( \\lfloor \\frac{n}{S} \\rfloor! \\right)^{S - r} \\left[ \\left( \\lfloor \\frac{n}{S} \\rfloor + 1 \\right)! \\right]^{r}} : r=n−S⌊nS⌋r = n - S \\lfloor \\frac{n}{S} \\rfloor.","code":"index_brillouin(woodland) #> [1] 2.230661 index_brillouin(woodland, evenness = TRUE) #> [1] 0.8025508"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"dominance-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness","what":"Dominance index","title":"alpha Diversity","text":"following methods return dominance index, reciprocal inverse form usually adopted, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"simpson-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Dominance index","what":"Simpson index","title":"alpha Diversity","text":"Simpson index (Simpson 1949) expresses probability two individuals randomly picked finite sample belong two different types. can interpreted weighted mean proportional abundances. metric true probability value, ranges 00 (taxa equally present) 11 (one taxon dominates community completely). Dominance infinite sample: D=∑=1Spi2 D = \\sum_{= 1}^{S} p_i^2 Dominance finite sample: D=∑=1Sni(ni−1)n(n−1) D = \\sum_{= 1}^{S} \\frac{n_i \\left( n_i - 1 \\right)}{n \\left( n - 1 \\right)}","code":"index_simpson(woodland) #> [1] 0.1199308 index_simpson(woodland, evenness = TRUE) #> [1] 0.4169071"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"mcintosh-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Dominance index","what":"McIntosh index","title":"alpha Diversity","text":"McIntosh index (McIntosh 1967) expresses heterogeneity sample geometric terms. describes sample point SS-dimensional hypervolume uses Euclidean distance point origin. Dominance: D=n−Un−n D = \\frac{n - U}{n - \\sqrt{n}} Evenness: E=n−Un−nS E = \\frac{n - U}{n - \\frac{n}{\\sqrt{S}}} UU distance sample origin SS dimensional hypervolume: U=∑=1Sni2U = \\sqrt{\\sum_{= 1}^{S} n_i^2}","code":"index_mcintosh(woodland) #> [1] 0.7079901 index_mcintosh(woodland, evenness = TRUE) #> [1] 0.8419571"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"berger-parker-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Dominance index","what":"Berger-Parker index","title":"alpha Diversity","text":"Berger-Parker index (Berger & Parker 1970) expresses proportional importance abundant type. metric highly biased sample size richness, moreover make use information available sample. Dominance: D=nmaxN D = \\frac{n_{max}}{N}","code":"index_berger(woodland) #> [1] 0.2058824"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"richness","dir":"Articles","previous_headings":"","what":"Richness","title":"alpha Diversity","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (RR) can used concept taxa number (McIntosh 1967). Richness refers variety taxa/species/types present assemblage community (Bobrowsky & Ball 1989) “number species present collection containing specified number individuals” (Hurlbert 1971). Richness measures (Bobrowsky & Ball 1989).","code":"index_margalef(woodland) #> [1] 3.699522 index_menhinick(woodland) #> [1] 1.53393"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"asymptotic-species-richness","dir":"Articles","previous_headings":"Richness","what":"Asymptotic Species Richness","title":"alpha Diversity","text":"Estimators asymptotic richness based frequencies rare species original sampling data.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"chao1-estimator-chao-1984","dir":"Articles","previous_headings":"Richness > Asymptotic Species Richness","what":"Chao1 estimator (Chao 1984)","title":"alpha Diversity","text":"ŜChao1={S+n−1nF122F2F2>0S+n−1nF1(F1−1)2F2=0 \\hat{S}_{Chao1} = \\begin{cases} S + \\frac{n - 1}{n} \\frac{F_1^2}{2 F_2} & F_2 > 0 \\\\ S + \\frac{n - 1}{n} \\frac{F_1 (F_1 - 1)}{2} & F_2 = 0 \\end{cases} F1F_1 number singleton species F2F_2 number doubleton species. special case homogeneous case, bias-corrected estimator : ŜChao1=S+n−1nF1(F1−1)2F2+1 \\hat{S}_{Chao1} = S + \\frac{n - 1}{n} \\frac{F_1 (F_1 - 1)}{2 F_2 + 1} improved Chao1 estimator (Chiu et al. 2014) makes use additional information tripletons (F3F_3) quadrupletons (F4F_4): ŜiChao1=ŜChao1+n−34nF3F4×max(F1−n−3n−1F2F32F4,0) \\hat{S}_{iChao1} = \\hat{S}_{Chao1} + \\frac{n - 3}{4 n} \\frac{F_3}{F_4} \\times \\max\\left(F_1 - \\frac{n - 3}{n - 1} \\frac{F_2 F_3}{2 F_4} , 0\\right)","code":"index_chao1(woodland) #> [1] 22.65098 index_chao1(woodland, unbiased = TRUE) #> [1] 21.49118 index_chao1(woodland, improved = TRUE)"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"abundance-based-coverage-estimator-chao-lee-1992","dir":"Articles","previous_headings":"Richness > Asymptotic Species Richness","what":"Abundance-based Coverage Estimator (Chao & Lee 1992)","title":"alpha Diversity","text":"ŜACE=Ŝabun+ŜrareĈACE+F1ĈACE×γ̂ACE2 \\hat{S}_{ACE} = \\hat{S}_{abun} + \\frac{\\hat{S}_{rare}}{\\hat{C}_{ACE}} + \\frac{F_1}{\\hat{C}_{ACE}} \\times \\hat{\\gamma}^2_{ACE} Ŝrare=∑=1kFi\\hat{S}_{rare} = \\sum_{= 1}^{k} F_i number rare taxa, Ŝabun=∑>kNFi\\hat{S}_{abun} = \\sum_{> k}^{N} F_i number abundant taxa (given cut-value kk), ĈACE=1−F1nrare\\hat{C}_{ACE} = 1 - \\frac{F_1}{n_{rare}} Turing’s coverage estimate : γ̂ACE2=max[ŜrareĈACE∑=1ki(−1)Finrare(nrare−1)−1,0] \\hat{\\gamma}^2_{ACE} = \\max\\left[\\frac{\\hat{S}_{rare}}{\\hat{C}_{ACE}} \\frac{\\sum_{= 1}^{k} (- 1)F_i}{n_{rare}\\left(n_{rare} - 1\\right)} - 1, 0\\right]","code":"index_ace(woodland) #> [1] 22.0875"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"squares-estimator-alroy-2018","dir":"Articles","previous_headings":"Richness > Asymptotic Species Richness","what":"Squares estimator (Alroy 2018)","title":"alpha Diversity","text":"squares richness estimator designed accurate Chao1 abundance distributions even: Ŝ=S+F12n2−F1×S∑=1Sni2 \\hat{S} = S + \\frac{F_1^2}{n^2 - F_1 \\times S} \\sum_{= 1}^{S} n_i^2","code":"index_squares(woodland) #> [1] 21.92422"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"quadrat-richness","dir":"Articles","previous_headings":"Richness","what":"Quadrat richness","title":"alpha Diversity","text":"replicated incidence data (.e. presence/absence data two sampled quadrats equal size; m×pm \\times p incidence matrix), Chao2 estimator :","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"chao2-estimator-chao-1987","dir":"Articles","previous_headings":"Richness > Quadrat richness","what":"Chao2 estimator (Chao 1987)","title":"alpha Diversity","text":"ŜChao2={S+m−1mq122q2q2>0S+m−1mq1(q1−1)2q2=0 \\hat{S}_{Chao2} = \\begin{cases} S + \\frac{m - 1}{m} \\frac{q_1^2}{2 q_2} & q_2 > 0 \\\\ S + \\frac{m - 1}{m} \\frac{q_1 (q_1 - 1)}{2} & q_2 = 0 \\end{cases} Improved Chao2 estimator (Chiu et al. 2014): ŜiChao2=ŜChao2+m−34mq3q4×max(q1−m−3m−1q2q32q4,0) \\hat{S}_{iChao2} = \\hat{S}_{Chao2} + \\frac{m - 3}{4 m} \\frac{q_3}{q_4} \\times \\max\\left(q_1 - \\frac{m - 3}{m - 1} \\frac{q_2 q_3}{2 q_4} , 0\\right)","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"incidence-based-coverage-estimator-chao-chiu-2016","dir":"Articles","previous_headings":"Richness > Quadrat richness","what":"Incidence-based Coverage Estimator (Chao & Chiu 2016)","title":"alpha Diversity","text":"ŜICE=Ŝfreq+ŜinfreqĈinfreq+q1Ĉinfreq×γ̂infreq2 \\hat{S}_{ICE} = \\hat{S}_{freq} + \\frac{\\hat{S}_{infreq}}{\\hat{C}_{infreq}} + \\frac{q_1}{\\hat{C}_{infreq}} \\times \\hat{\\gamma}^2_{infreq} Ŝinfreq=∑=1kqi\\hat{S}_{infreq} = \\sum_{= 1}^{k} q_i number infrequent taxa, Ŝfreq=∑>kNqi\\hat{S}_{freq} = \\sum_{> k}^{N} q_i number frequent taxa (given cut-value kk), Ĉinfreq=1−Q1∑=1kiqi\\hat{C}_{infreq} = 1 - \\frac{Q_1}{\\sum_{= 1}^{k} iq_i} Turing’s coverage estimate : γ̂infreq2=max[ŜinfreqĈinfreqminfreqminfreq−1∑=1ki(−1)qi(∑=1kiqi)(∑=1kiqi−1)−1,0] \\hat{\\gamma}^2_{infreq} = \\max\\left[\\frac{\\hat{S}_{infreq}}{\\hat{C}_{infreq}} \\frac{m_{infreq}}{m_{infreq} - 1} \\frac{\\sum_{= 1}^{k} (- 1)q_i}{\\left(\\sum_{= 1}^{k} iq_i\\right)\\left(\\sum_{= 1}^{k} iq_i - 1\\right)} - 1, 0\\right] minfreqm_{infreq} number sampling units include least one infrequent species.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"rarefaction","dir":"Articles","previous_headings":"","what":"Rarefaction","title":"alpha Diversity","text":"always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (Ŝ\\hat{S}) number taxa expected samples standard size nn (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":"## Baxter rarefaction RA <- rarefaction(mississippi, sample = 100, method = \"baxter\") plot(RA)"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"hurlbert-1971-unbiased-estimate-of-sander-1968-rarefaction","dir":"Articles","previous_headings":"Rarefaction","what":"Hurlbert (1971) unbiased estimate of Sander (1968) rarefaction","title":"alpha Diversity","text":"E(S)=∑=1S[1−(n−nim)(nm)] E(S) = \\sum_{= 1}^{S} \\left[ 1 - \\frac{{n - n_i} \\choose m}{n \\choose m} \\right]","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"baxter-2001-rarefaction","dir":"Articles","previous_headings":"Rarefaction","what":"Baxter (2001) rarefaction","title":"alpha Diversity","text":"E(S)=∑=1S[1−(n−ni)!(n−m)!(n−ni−m)!n!] E(S) = \\sum_{= 1}^{S} \\left[ 1 - \\frac{(n - n_i)!(n - m)!}{(n - n_i - m)!n!} \\right] : SS number observed species/types, nin_i number individuals ii-th species/type, n=∑=1Snin = \\sum_{= 1}^{S} n_i total number individuals, mm sub-sample size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"alpha Diversity","text":"Alroy, J. (2018). Limits Species Richness Terrestrial Communities. Ecology Letters, 21(12): 1781-1789. 10.1111/ele.13152. Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4): 715-725. DOI: 10.2307/2694184. Berger, W. H. & Parker, F. L. (1970). Diversity Planktonic Foraminifera Deep Sea Sediments. Science, 168(3937): 1345-1347. DOI: 10.1126/science.168.3937.1345. Bobrowsky, P. T. & Ball, B. F. (1989). Theory Mechanics Ecological Diversity Archaeology. R. D. Leonard & G. T. Jones (Eds.), Quantifying Diversity Archaeology, 4-12. New Directions Archaeology. Cambridge: Cambridge University Press. Bowman, K. O., Hutcheson, K., Odum, E. P. & Shenton, L. R. (1971). Comments Distribution Indices Diversity. E. C. Patil, E. C. Pielou & W. E. Waters (Eds.), Statistical Ecology, 3:315-366. University Park, PA: Pennsylvania State University Press. Brillouin, L. (1956). Science Information Theory. New York: Academic Press. Chao, . (1984). Nonparametric Estimation Number Classes Population. Scandinavian Journal Statistics, 11(4): 265-270. Chao, . (1987). Estimating Population Size Capture Recapture Data Unequal Catchability. Biometrics, 43(4): 783-791. DOI: 10.2307/2531532. Chao, . & Lee, S.-M. (1992). Estimating Number Classes Via Sample Coverage. Journal American Statistical Association, 87(417): 210-217. DOI: 10.1080/01621459.1992.10475194. Chao, . & Chiu, C.-H. (2016). Species Richness: Estimation Comparison. N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri & J. L. Teugels (Eds.), Wiley StatsRef: Statistics Reference Online, 1-26. Chichester, UK: John Wiley & Sons, Ltd. DOI: 10.1002/9781118445112.stat03432.pub2. Chiu, C.-H., Wang, Y.-T., Walther, B. . & Chao, . (2014). Improved Nonparametric Lower Bound Species Richness Via Modified Good-Turing Frequency Formula. Biometrics, 70(3): 671-682. DOI: 10.1111/biom.12200. de Caprariis, P., Lindemann, R. H. & Collins, C. M. (1976). Method Determining Optimum Sample Size Species Diversity Studies. Journal International Association Mathematical Geology, 8(5): 575-581. DOI: 10.1007/BF01042995. Fisher, R. ., Corbet, . S. & Williams, C. B. (1943). Relation Number Species Number Individuals Random Sample Animal Population. Journal Animal Ecology, 12(1): 42. DOI: 10.2307/1411. Gleason, H. . (1922). Relation Species Area. Ecology, 3(2): 158-162. DOI: 10.2307/1929150. Hurlbert, S. H. (1971). Nonconcept Species Diversity: Critique Alternative Parameters. Ecology, 52(4): 577-586. DOI: 10.2307/1934145. Hutcheson, K. (1970). Test Comparing Diversity Based Shannon Formula. Journal Theoretical Biology, 29(1): 151-154. DOI: 10.1016/0022-5193(70)90124-4. Kilburn, P. D. (1966). Analysis Species-Area Relation. Ecology, 47(5): 831-843. DOI: 10.2307/1934269. Laxton, R. R. (1978). Measure Diversity. Journal Theoretical Biology, 70(1): 51-67. DOI: 10.1016/0022-5193(78)90302-8. Macarthur, R. H. (1965). Patterns Species Diversity. Biological Reviews, 40(4): 510-533. DOI: 10.1111/j.1469-185X.1965.tb00815.x. Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. Margalef, R. (1958). Information Theory Ecology. General Systems, 3: 36-71. McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3): 392-404. DOI: 10.2307/1932674. Menhinick, E. F. 1964. Comparison Species-Individuals Diversity Indices Applied Samples Field Insects. Ecology, 45(4): 859-861. 10.2307/1934933. Odum, H. T., Cantlon, J. E. Kornicker, L. S. (1960). Organizational Hierarchy Postulate Interpretation Species-Individual Distributions, Species Entropy, Ecosystem Evolution, Meaning Species-Variety Index. Ecology, 41(2): 395-395. 10.2307/1930248. Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. DOI: 10.1146/annurev.es.05.110174.001441. Pielou, E. C. (1975). Ecological Diversity. New York: Wiley. Preston, F. W. (1948). Commonness, Rarity, Species. Ecology, 29(3): 254-283. DOI: 10.2307/1930989. Preston, F. W. (1962a). Canonical Distribution Commonness Rarity: Part . Ecology, 43(2): 185. DOI: 10.2307/1931976. Preston, F. W. (1962b). Canonical Distribution Commonness Rarity: Part II. Ecology, 43(3): 410-432. DOI: 10.2307/1933371. Sander, H. L. (1968). Marine Benthic Diversity: Comparative Study. American Naturalist, 102(925): 243-282. Shannon, C. E. (1948). Mathematical Theory Communication. Bell System Technical Journal, 27, 379-423. DOI: 10.1002/j.1538-7305.1948.tb01338.x. Simpson, E. H. (1949). Measurement Diversity. Nature, 163(4148): 688-688. DOI: 10.1038/163688a0.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/beta.html","id":"turnover","dir":"Articles","previous_headings":"","what":"Turnover","title":"beta Diversity","text":"following methods can used ascertain degree turnover taxa composition along gradient qualitative (presence/absence) data. assumes order matrix rows (1 mm) follows progression along gradient/transect. denote m×pm \\times p incidence matrix X=[xij]∀∈[1,m],j∈[1,p]X = \\left[ x_{ij} \\right] ~\\forall \\\\left[ 1,m \\right], j \\\\left[ 1,p \\right] p×pp \\times p corresponding co-occurrence matrix Y=[yij]∀,j∈[1,p]Y = \\left[ y_{ij} \\right] ~\\forall ,j \\\\left[ 1,p \\right], row column sums: xi⋅=∑j=1pxijx⋅j=∑=1mxijx⋅⋅=∑j=1p∑=1mxij∀xij∈{0,1}yi⋅=∑j≥ipyijy⋅j=∑≤jpyijy⋅⋅=∑=1p∑j≥ipyij∀yij∈{0,1}\\begin{align} x_{\\cdot} = \\sum_{j = 1}^{p} x_{ij} && x_{\\cdot j} = \\sum_{= 1}^{m} x_{ij} && x_{\\cdot \\cdot} = \\sum_{j = 1}^{p} \\sum_{= 1}^{m} x_{ij} && \\forall x_{ij} \\\\lbrace 0,1 \\rbrace \\\\ y_{\\cdot} = \\sum_{j \\geqslant }^{p} y_{ij} && y_{\\cdot j} = \\sum_{\\leqslant j}^{p} y_{ij} && y_{\\cdot \\cdot} = \\sum_{= 1}^{p} \\sum_{j \\geqslant }^{p} y_{ij} && \\forall y_{ij} \\\\lbrace 0,1 \\rbrace \\end{align} Turnover measures. : α\\alpha mean sample diversity: α=x⋅⋅m\\alpha = \\frac{x_{\\cdot \\cdot}}{m}, g(H)g(H) number taxa gained along transect, l(H)l(H) number taxa lost along transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/beta.html","id":"similarity","dir":"Articles","previous_headings":"","what":"Similarity","title":"beta Diversity","text":"Similarity two samples aa bb two types xx yy can measured follow. indices provide scale similarity 00-11 11 perfect similarity 00 similarity, exception Brainerd-Robinson index scaled 00 200200. Qualitative similarity measures (samples). Quantitative similarity measures (samples). Quantitative similarity measures (types). : SaS_a SbS_b denote total number taxa observed samples aa bb, respectively, NaN_a NbN_b denote total number individuals samples aa bb, respectively, aja_j bjb_j denote number individuals jj-th type/taxon, j∈[1,S]j \\\\left[ 1,S \\right], xix_i yiy_i denote number individuals ii-th sample/case, ∈[1,m]\\\\left[ 1,m \\right], oio_i denotes number sample/case common type/taxon: oi=∑k=1mxk∩yko_i = \\sum_{k = 1}^{m} x_k \\cap y_k, ojo_j denotes number type/taxon common sample/case: oj=∑k=1Sak∩bko_j = \\sum_{k = 1}^{S} a_k \\cap b_k.","code":"## Brainerd-Robinson (similarity between assemblages) BR <- similarity(mississippi, method = \"brainerd\") plot_spot(BR, col = khroma::colour(\"YlOrBr\")(12)) ## Binomial co-occurrence (similarity between types) BI <- similarity(mississippi, method = \"binomial\") plot_spot(BI, col = khroma::colour(\"PRGn\")(12))"},{"path":"https://packages.tesselle.org/tabula/dev/articles/beta.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"beta Diversity","text":"Brainerd, G. W. 1951. Place Chronological Ordering Archaeological Analysis. American Antiquity, 16(4), 301-313. DOI: 10.2307/276979. Bray, J. R. & Curtis, J. T. (1957). Ordination Upland Forest Communities Southern Wisconsin. Ecological Monographs, 27(4), 325-349. DOI: 10.2307/1942268. Cody, M. L. (1975). Towards Theory Continental Species Diversity: Bird Distributions Mediterranean Habitat Gradients. M. L. Cody & J. M. Diamond (Eds.), Ecology Evolution Communities, 214-257. Cambridge, MA: Harvard University Press. Kintigh, K. (2006). Ceramic Dating Type Associations. J. Hantman & R. (Eds.), Managing Archaeological Data: Essays Honor Sylvia W. Gaines, 17–26. Anthropological Research Paper 57. Tempe, AZ: Arizona State University. DOI: 10.6067/XCV8J38QSS. Moreno, C. E. & Rodríguez, P. (2010). Consistent Terminology Quantifying Species Diversity? Oecologia, 163(2), 279-782. DOI: 10.1007/s00442-010-1591-7. Robinson, W. S. (1951). Method Chronologically Ordering Archaeological Deposits. American Antiquity, 16(4), 293-301. DOI: 10.2307/276978. Routledge, R. D. (1977). Whittaker’s Components Diversity. Ecology, 58(5), 1120-1127. DOI: 10.2307/1936932. Whittaker, R. H. (1960). Vegetation Siskiyou Mountains, Oregon California. Ecological Monographs, 30(3), 279-338. DOI: 10.2307/1943563.. Wilson, M. V. & Shmida, . (1984). Measuring Beta Diversity Presence-Absence Data. Journal Ecology, 72(3), 1055-1064. DOI: 10.2307/2259551.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Analysis and Visualization of Archaeological Count Data","text":"easy way examine archaeological count data. package provides several tests measures diversity: heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). allows easily visualize count data statistical thresholds: rank vs. abundance plots, heatmaps, Ford (1962) Bertin (1977) diagrams, etc. tabula provides methods : Diversity measurement: heterogeneity(), evenness(), richness(), rarefaction(), turnover(). Similarity measurement co-occurrence: similarity(), occurrence(). Assessing sample size significance: bootstrap(), jackknife(), simulate(). Bertin (1977) Ford (1962) (battleship curve) diagrams: plot_bertin(), plot_ford(). Seriograph (Desachy 2004): seriograph(), matrigraph(). Heatmaps: plot_heatmap(), plot_spot(). kairos companion package tabula provides functions chronological modeling dating archaeological assemblages count data. cite tabula publications use: Frerebeau N (2019). “tabula: R Package Analysis, Seriation, Visualization Archaeological Count Data.” Journal Open Source Software, 4(44). doi:10.21105/joss.01821 https://doi.org/10.21105/joss.01821. Frerebeau N (2024). tabula: Analysis Visualization Archaeological Count Data. Université Bordeaux Montaigne, Pessac, France. doi:10.5281/zenodo.1489944 https://doi.org/10.5281/zenodo.1489944, R package version 3.1.0, https://packages.tesselle.org/tabula/. package part tesselle project https://www.tesselle.org.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analysis and Visualization of Archaeological Count Data","text":"can install released version tabula CRAN : development version GitHub :","code":"install.packages(\"tabula\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/tabula\")"},{"path":"https://packages.tesselle.org/tabula/dev/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Analysis and Visualization of Archaeological Count Data","text":"assumes keep data tidy: variable (type/taxa) must saved column observation (sample/case) must saved row.","code":"## Install extra packages (if needed) # install.packages(\"folio\") ## Load packages library(tabula) ## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\") ## Ford diagram plot_ford(mississippi) ## Co-occurrence of ceramic types mississippi |> occurrence() |> plot_spot() ## Data from Conkey 1980, Kintigh 1989, p. 28 data(\"chevelon\", package = \"folio\") ## Measure diversity by comparing to simulated assemblages set.seed(12345) chevelon |> heterogeneity(method = \"shannon\") |> simulate() |> plot() chevelon |> richness(method = \"count\") |> simulate() |> plot()"},{"path":"https://packages.tesselle.org/tabula/dev/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Analysis and Visualization of Archaeological Count Data","text":"Please note tabula project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/paper.html","id":null,"dir":"","previous_headings":"","what":"Background","title":"Background","text":"Detecting quantifying material cultural variations time space important methodological issues archaeology. solve issues, need construct reliable chronologies quantitative descriptions archaeological assemblages, . e. archaeological sites intrasite units, described set pp different objects. Building chronologies involves distinguishing relative (providing chronological sequence) absolute dating methods (yield calendric indicators) [@obrien2002]. Within relative dating, matrix seriation long-established method—first formulated @petrie1899—allowed construction reference chronologies [@ihm2005]. set XX nn archaeological assemblages, seriation problem comes discovering XX order inferred chronological. approach relies set well-defined statistical archaeological assumptions [@dunnell1970]. may use priori information, e.g., absolute dates stratigraphical constraints [@poblome2003] allows analysis chronological patterns socio-economic cultural perspective (e.g., [@bellanger2012], [@lipo2015]). quantitative analysis archaeological assemblages can thus carried synchronic, e.g., diversity measurements, diachronic, e.g., evolutionary studies: selection process, patterns cultural transmission, etc., way. approaches cover wide range applications led development multitude statistical models, none systematically implemented enable deployment reproducible workflows.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/paper.html","id":"summary","dir":"","previous_headings":"","what":"Summary","title":"Background","text":"tabula provides convenient reproducible toolkit analyzing, seriating, visualizing archaeological count data, artifacts faunal remains. Several R packages, e.g. ade4 [@dray2007], SpadeR [@chao2016] vegan [@oksanen2019], allow estimation diversity indices implement seriation/ordination methods, packages mainly oriented towards ecological issues. tabula provides archaeologically-orientated implementations allow integration specific data (dates, stratigraphy, etc.) offers consistent framework. latter particular value since tabula designed used archaeologists students little background courses dating methods applied statistics archaeology. package uses set S4 classes archaeological data matrices extend matrix data type. new classes represent different specialized matrices: incidence, abundance, co-occurrence, (dis)similarity. Methods variety functions applied objects classes provide tools relative absolute dating analysis (chronological) patterns. tabula includes functions matrix seriation (seriate_*), well chronological modeling dating (date_*) archaeological assemblages objects. Resulting models can checked stability refined resampling methods (refine_*). Estimated dates can displayed tempo activity plot [@dye2016] assess rhythms long periods. Beyond , tabula provides several tests (test_*) measures diversity within archaeological assemblages (index_*): heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). Finally, package makes easy visualize count data statistical thresholds (plot_*): rank vs. abundance plots, heatmaps, @ford1962 @bertin1977 diagrams.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/paper.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Background","text":"following contributors made possible develop project helpful discussion bringing new ideas: Jean-Baptiste Fourvel, Brice Lebrun, Ben Marwick, Matthew Peeples, Anne Philippe.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/CompositionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Composition Index — CompositionIndex-class","title":"Composition Index — CompositionIndex-class","text":"S4 class represent composition measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/CompositionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Composition Index — CompositionIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/CompositionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Composition Index — CompositionIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Index — DiversityIndex-class","title":"Diversity Index — DiversityIndex-class","text":"S4 class represent diversity measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Diversity Index — DiversityIndex-class","text":".Data numeric vector diversity measures. labels character vector giving sample names. size integer vector giving sample sizes. data numeric matrix count data. method character string specifying method used. simulation four columns numeric matrix giving diversity measures simulated assemblage (sample size, mean estimate, lower upper boundaries confidence interval).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Diversity Index — DiversityIndex-class","text":"class inherits base numeric.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Diversity Index — DiversityIndex-class","text":"code snippets , x DiversityIndex object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Index — DiversityIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/EvennessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenness Index — EvennessIndex-class","title":"Evenness Index — EvennessIndex-class","text":"S4 class represent evenness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/EvennessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Evenness Index — EvennessIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/EvennessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evenness Index — EvennessIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/HeterogeneityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity Index — HeterogeneityIndex-class","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"S4 class represent heterogeneity measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/HeterogeneityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/HeterogeneityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RarefactionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Index — RarefactionIndex-class","title":"Rarefaction Index — RarefactionIndex-class","text":"S4 class represent rarefaction measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RarefactionIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rarefaction Index — RarefactionIndex-class","text":".Data numeric matrix count data (expected taxa per fixed number individuals). labels character vector giving sample names. size integer vector giving sample sizes. method character string specifying method used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RarefactionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rarefaction Index — RarefactionIndex-class","text":"class inherits base matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/RarefactionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Index — RarefactionIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RichnessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness Index — RichnessIndex-class","title":"Richness Index — RichnessIndex-class","text":"S4 class represent richness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RichnessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Richness Index — RichnessIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/RichnessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness Index — RichnessIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/aves.html","id":null,"dir":"Reference","previous_headings":"","what":"Birds Species and Abundances — aves","title":"Birds Species and Abundances — aves","text":"dataset birds species abundances managed unmanaged areas along River Wye (UK).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/aves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Birds Species and Abundances — aves","text":"","code":"aves"},{"path":"https://packages.tesselle.org/tabula/dev/reference/aves.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birds Species and Abundances — aves","text":"data.frame 2 rows 26 variables (bird species).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/aves.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birds Species and Abundances — aves","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Estimation — bootstrap","title":"Bootstrap Estimation — bootstrap","text":"Samples randomly elements object replacement.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Estimation — bootstrap","text":"","code":"# S4 method for class 'DiversityIndex' bootstrap(object, n = 1000, f = NULL)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Estimation — bootstrap","text":"object R object (typically DiversityIndex object). n non-negative integer giving number bootstrap replications. f function takes single numeric vector (result ) argument.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Estimation — bootstrap","text":"f NULL (default), bootstrap() returns named numeric vector following elements: original observed value applied object. mean bootstrap estimate mean . bias bootstrap estimate bias . error bootstrap estimate standard error . f function, bootstrap() returns result f applied n values .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Estimation — bootstrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Estimation — bootstrap","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187 ## Bootstrap resampling bootstrap(h, f = NULL) #> original mean bias error #> Altamira 3.269200 3.278498 0.009297979 0.08048385 #> Cueto de la Mina 2.955298 2.972580 0.017281827 0.15700290 #> El Juyo 2.491683 2.521575 0.029891792 0.19021698 #> El Cierro 2.485604 2.487144 0.001539771 0.22464720 #> La Paloma 2.329187 2.310936 -0.018250257 0.26487280 bootstrap(h, f = summary) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> Altamira 2.992719 3.222878 3.280765 3.276743 3.334473 3.549729 #> Cueto de la Mina 2.356353 2.894522 2.983316 2.977874 3.078325 3.369425 #> El Juyo 1.721241 2.376338 2.521911 2.515313 2.655347 3.117557 #> El Cierro 1.559581 2.351641 2.510941 2.486987 2.634027 2.978305 #> La Paloma 1.273028 2.151884 2.334882 2.308805 2.491505 2.931322 quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #> 25% 50% #> Altamira 3.229277 3.287153 #> Cueto de la Mina 2.888914 2.986238 #> El Juyo 2.406740 2.535562 #> El Cierro 2.341829 2.499437 #> La Paloma 2.152664 2.339654"},{"path":"https://packages.tesselle.org/tabula/dev/reference/cantabria.html","id":null,"dir":"Reference","previous_headings":"","what":"Early Magdalenian Engraved Bones — cantabria","title":"Early Magdalenian Engraved Bones — cantabria","text":"dataset design elements engraved bones Cantabrian Spain.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/cantabria.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Early Magdalenian Engraved Bones — cantabria","text":"","code":"cantabria"},{"path":"https://packages.tesselle.org/tabula/dev/reference/cantabria.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Early Magdalenian Engraved Bones — cantabria","text":"data.frame 5 rows 44 variables (designs).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/cantabria.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Early Magdalenian Engraved Bones — cantabria","text":"Conkey, M. W. (1980). Identification prehistoric hunter-gatherer aggregation sites: case Altamira. Current Anthropology, 21(5), 609-630. Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness — heterogeneity","title":"Heterogeneity and Evenness — heterogeneity","text":"heterogeneity() computes heterogeneity dominance index. evenness() computes evenness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heterogeneity and Evenness — heterogeneity","text":"","code":"heterogeneity(object, ...) evenness(object, ...) # S4 method for class 'matrix' heterogeneity( object, ..., method = c(\"berger\", \"boone\", \"brillouin\", \"mcintosh\", \"shannon\", \"simpson\") ) # S4 method for class 'data.frame' heterogeneity( object, ..., method = c(\"berger\", \"boone\", \"brillouin\", \"mcintosh\", \"shannon\", \"simpson\") ) # S4 method for class 'matrix' evenness( object, ..., method = c(\"shannon\", \"brillouin\", \"mcintosh\", \"simpson\") ) # S4 method for class 'data.frame' evenness( object, ..., method = c(\"shannon\", \"brillouin\", \"mcintosh\", \"simpson\") )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heterogeneity and Evenness — heterogeneity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods (see ). method character string specifying index computed (see details). unambiguous substring can given. evenness logical scalar: evenness measure computed instead heterogeneity/dominance index?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heterogeneity and Evenness — heterogeneity","text":"heterogeneity() returns HeterogeneityIndex object. evenness() returns EvennessIndex object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Heterogeneity and Evenness — heterogeneity","text":"Diversity measurement assumes individuals specific taxa equivalent types equally different (Peet 1974). measure diversity can achieved using indices built relative abundance taxa. indices (sometimes referred non-parametric indices) benefit making assumptions underlying distribution taxa abundance: take relative abundances species present species richness account. Peet (1974) refers indices heterogeneity. Diversity indices focus one aspect taxa abundance emphasize either richness (weighting towards uncommon taxa) dominance (weighting towards abundant taxa; Magurran 1988). Evenness measure evenly individuals distributed across sample.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"heterogeneity-and-evenness-measures","dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness Measures","title":"Heterogeneity and Evenness — heterogeneity","text":"following heterogeneity index corresponding evenness measures available (see Magurran 1988 details): berger Berger-Parker dominance index. boone Boone heterogeneity measure. brillouin Brillouin diversity index. mcintosh McIntosh dominance index. shannon Shannon-Wiener diversity index. simpson Simpson dominance index. berger, mcintosh simpson methods return dominance index, reciprocal inverse form usually adopted, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Heterogeneity and Evenness — heterogeneity","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity and Evenness — heterogeneity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heterogeneity and Evenness — heterogeneity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187 (e <- evenness(cantabria, method = \"shannon\")) #> [1] 0.8987278 0.8966760 0.8462335 0.9178574 0.9373336 plot(h)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":null,"dir":"Reference","previous_headings":"","what":"Abundance-based Coverage Estimator — index_ace","title":"Abundance-based Coverage Estimator — index_ace","text":"Abundance-based Coverage Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abundance-based Coverage Estimator — index_ace","text":"","code":"index_ace(x, ...) # S4 method for class 'numeric' index_ace(x, k = 10, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abundance-based Coverage Estimator — index_ace","text":"x numeric vector count data (absolute frequencies). ... Currently used. k length-one numeric vector giving threshold rare/infrequent abundant/frequent species. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Abundance-based Coverage Estimator — index_ace","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Abundance-based Coverage Estimator — index_ace","text":"Chao, . & Lee, S.-M. (1992). Estimating Number Classes via Sample Coverage. Journal American Statistical Association, 87(417), 210-217. doi:10.1080/01621459.1992.10475194 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Abundance-based Coverage Estimator — index_ace","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":null,"dir":"Reference","previous_headings":"","what":"Baxter's Rarefaction — index_baxter","title":"Baxter's Rarefaction — index_baxter","text":"Baxter's Rarefaction","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Baxter's Rarefaction — index_baxter","text":"","code":"index_baxter(x, ...) # S4 method for class 'numeric' index_baxter(x, sample, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Baxter's Rarefaction — index_baxter","text":"x numeric vector count data (absolute frequencies). ... Currently used. sample length-one numeric vector giving sub-sample size. size sample smaller total community size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Baxter's Rarefaction — index_baxter","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Baxter's Rarefaction — index_baxter","text":"Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Baxter's Rarefaction — index_baxter","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":null,"dir":"Reference","previous_headings":"","what":"Berger-Parker Dominance Index — index_berger","title":"Berger-Parker Dominance Index — index_berger","text":"Berger-Parker Dominance Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Berger-Parker Dominance Index — index_berger","text":"","code":"index_berger(x, ...) # S4 method for class 'numeric' index_berger(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Berger-Parker Dominance Index — index_berger","text":"x numeric vector count data (absolute frequencies). ... Currently used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Berger-Parker Dominance Index — index_berger","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Berger-Parker Dominance Index — index_berger","text":"Berger-Parker index expresses proportional importance abundant type. metric highly biased sample size richness, moreover make use information available sample. dominance index, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Berger-Parker Dominance Index — index_berger","text":"Berger, W. H. & Parker, F. L. (1970). Diversity Planktonic Foraminifera Deep-Sea Sediments. Science, 168(3937), 1345-1347. doi:10.1126/science.168.3937.1345 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Berger-Parker Dominance Index — index_berger","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Binomial Co-Occurrence Assessment — index_binomial","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"Binomial Co-Occurrence Assessment","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"","code":"index_binomial(x, y, ...) # S4 method for class 'numeric,numeric' index_binomial(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"assesses degree co-occurrence taxa/types within dataset. strongest associations shown large positive numbers, strongest segregations large negative numbers.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"Kintigh, K. (2006). Ceramic Dating Type Associations. J. Hantman R. (eds.), Managing Archaeological Data: Essays Honor Sylvia W. Gaines. Anthropological Research Paper, 57. Tempe, AZ: Arizona State University, p. 17-26.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":null,"dir":"Reference","previous_headings":"","what":"Boone Heterogeneity Measure — index_boone","title":"Boone Heterogeneity Measure — index_boone","text":"Boone Heterogeneity Measure","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boone Heterogeneity Measure — index_boone","text":"","code":"index_boone(x, ...) # S4 method for class 'matrix' index_boone(x, j = NULL, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boone Heterogeneity Measure — index_boone","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used. j integer giving index reference type/taxa. NULL (default), frequent type/taxa assemblage used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boone Heterogeneity Measure — index_boone","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Boone Heterogeneity Measure — index_boone","text":"Boone, J. L. (1987). Defining Measuring Midden Catchment. American Antiquity, 52(2), 336-45. doi:10.2307/281785 . Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Boone Heterogeneity Measure — index_boone","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":null,"dir":"Reference","previous_headings":"","what":"Brainerd-Robinson Quantitative Index — index_brainerd","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"Brainerd-Robinson Quantitative Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"","code":"index_brainerd(x, y, ...) # S4 method for class 'numeric,numeric' index_brainerd(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"city-block metric similarity pairs samples/cases.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"Brainerd, G. W. (1951). Place Chronological Ordering Archaeological Analysis. American Antiquity, 16(04), 301-313. doi:10.2307/276979 . Robinson, W. S. (1951). Method Chronologically Ordering Archaeological Deposits. American Antiquity, 16(04), 293-301. doi:10.2307/276978 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorenson Quantitative Index — index_bray","title":"Sorenson Quantitative Index — index_bray","text":"Bray Curtis modified version Sorenson index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorenson Quantitative Index — index_bray","text":"","code":"index_bray(x, y, ...) # S4 method for class 'numeric,numeric' index_bray(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorenson Quantitative Index — index_bray","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorenson Quantitative Index — index_bray","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sorenson Quantitative Index — index_bray","text":"Bray, J. R. & Curtis, J. T. (1957). Ordination Upland Forest Communities Southern Wisconsin. Ecological Monographs, 27(4), 325-349. doi:10.2307/1942268 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sorenson Quantitative Index — index_bray","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":null,"dir":"Reference","previous_headings":"","what":"Brillouin Diversity Index. — index_brillouin","title":"Brillouin Diversity Index. — index_brillouin","text":"Brillouin Diversity Index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Brillouin Diversity Index. — index_brillouin","text":"","code":"index_brillouin(x, ...) # S4 method for class 'numeric' index_brillouin(x, evenness = FALSE, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Brillouin Diversity Index. — index_brillouin","text":"x numeric vector count data (absolute frequencies). ... Currently used. evenness numeric scalar: evenness computed? na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Brillouin Diversity Index. — index_brillouin","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Brillouin Diversity Index. — index_brillouin","text":"Brillouin index describes known collection: assume random sampling infinite population. Pielou (1975) Laxton (1978) argues use Brillouin index circumstances, especially preference Shannon index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Brillouin Diversity Index. — index_brillouin","text":"Ramanujan approximation used \\(x!\\) computation \\(x > 170\\).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Brillouin Diversity Index. — index_brillouin","text":"Brillouin, L. (1956). Science information theory. New York: Academic Press. Laxton, R. R. (1978). measure diversity. Journal Theoretical Biology, 70(1), 51-67. doi:10.1016/0022-5193(78)90302-8 . Pielou, E. C. (1975). Ecological Diversity. New York: Wiley. doi:10.4319/lo.1977.22.1.0174b","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Brillouin Diversity Index. — index_brillouin","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":null,"dir":"Reference","previous_headings":"","what":"Chao1 Estimator — index_chao1","title":"Chao1 Estimator — index_chao1","text":"Chao1 Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chao1 Estimator — index_chao1","text":"","code":"index_chao1(x, ...) # S4 method for class 'numeric' index_chao1(x, unbiased = FALSE, improved = FALSE, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chao1 Estimator — index_chao1","text":"x numeric vector count data (absolute frequencies). ... Currently used. unbiased logical scalar: bias-corrected estimator used? improved logical scalar: improved estimator used? na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Chao1 Estimator — index_chao1","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chao1 Estimator — index_chao1","text":"Chao, . (1984). Nonparametric Estimation Number Classes Population. Scandinavian Journal Statistics, 11(4), 265-270. Chiu, C.-H., Wang, Y.-T., Walther, B. . & Chao, . (2014). improved nonparametric lower bound species richness via modified good-turing frequency formula. Biometrics, 70(3), 671-682. doi:10.1111/biom.12200 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Chao1 Estimator — index_chao1","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":null,"dir":"Reference","previous_headings":"","what":"Chao2 Estimator — index_chao2","title":"Chao2 Estimator — index_chao2","text":"Chao2 Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chao2 Estimator — index_chao2","text":"","code":"index_chao2(x, ...) # S4 method for class 'matrix' index_chao2(x, unbiased = FALSE, improved = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chao2 Estimator — index_chao2","text":"x \\(m \\times p\\) matrix presence/absence data (incidence). ... Currently used. unbiased logical scalar: bias-corrected estimator used? improved logical scalar: improved estimator used?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Chao2 Estimator — index_chao2","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chao2 Estimator — index_chao2","text":"Chao, . (1987). Estimating Population Size Capture-Recapture Data Unequal Catchability. Biometrics 43(4), 783-791. Chiu, C.-H., Wang, Y.-T., Walther, B. . & Chao, . (2014). improved nonparametric lower bound species richness via modified good-turing frequency formula. Biometrics, 70(3), 671-682. doi:10.2307/2531532 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Chao2 Estimator — index_chao2","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":null,"dir":"Reference","previous_headings":"","what":"Cody Measure — index_cody","title":"Cody Measure — index_cody","text":"Cody Measure","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cody Measure — index_cody","text":"","code":"index_cody(x, ...) # S4 method for class 'matrix' index_cody(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cody Measure — index_cody","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cody Measure — index_cody","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cody Measure — index_cody","text":"assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cody Measure — index_cody","text":"Cody, M. L. (1975). Towards theory continental species diversity: Bird distributions Mediterranean habitat gradients. M. L. Cody & J. M. Diamond (Eds.), Ecology Evolution Communities. Cambridge, MA: Harvard University Press, p. 214-257.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cody Measure — index_cody","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":null,"dir":"Reference","previous_headings":"","what":"Hurlbert's Rarefaction — index_hurlbert","title":"Hurlbert's Rarefaction — index_hurlbert","text":"Hurlbert's unbiased estimate Sander's rarefaction.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hurlbert's Rarefaction — index_hurlbert","text":"","code":"index_hurlbert(x, ...) # S4 method for class 'numeric' index_hurlbert(x, sample, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hurlbert's Rarefaction — index_hurlbert","text":"x numeric vector count data (absolute frequencies). ... Currently used. sample length-one numeric vector giving sub-sample size. size sample smaller total community size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hurlbert's Rarefaction — index_hurlbert","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hurlbert's Rarefaction — index_hurlbert","text":"Hurlbert, S. H. (1971). Nonconcept Species Diversity: Critique Alternative Parameters. Ecology, 52(4), 577-586. doi:10.2307/1934145 . Sander, H. L. (1968). Marine Benthic Diversity: Comparative Study. American Naturalist, 102(925), 243-282.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Hurlbert's Rarefaction — index_hurlbert","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence-based Coverage Estimator — index_ice","title":"Incidence-based Coverage Estimator — index_ice","text":"Incidence-based Coverage Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence-based Coverage Estimator — index_ice","text":"","code":"index_ice(x, ...) # S4 method for class 'matrix' index_ice(x, k = 10, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence-based Coverage Estimator — index_ice","text":"x \\(m \\times p\\) matrix presence/absence data (incidence). ... Currently used. k length-one numeric vector giving threshold rare/infrequent abundant/frequent species.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence-based Coverage Estimator — index_ice","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incidence-based Coverage Estimator — index_ice","text":"Chao, . & Chiu, C.-H. (2016). Species Richness: Estimation Comparison. Balakrishnan, N., Colton, T., Everitt, B., Piegorsch, B., Ruggeri, F. & Teugels, J. L. (Eds.), Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd., 1-26. doi:10.1002/9781118445112.stat03432.pub2","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Incidence-based Coverage Estimator — index_ice","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":null,"dir":"Reference","previous_headings":"","what":"Jaccard Index — index_jaccard","title":"Jaccard Index — index_jaccard","text":"Jaccard Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jaccard Index — index_jaccard","text":"","code":"index_jaccard(x, y, ...) # S4 method for class 'character,character' index_jaccard(x, y) # S4 method for class 'logical,logical' index_jaccard(x, y) # S4 method for class 'numeric,numeric' index_jaccard(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jaccard Index — index_jaccard","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jaccard Index — index_jaccard","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Jaccard Index — index_jaccard","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jaccard Index — index_jaccard","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":null,"dir":"Reference","previous_headings":"","what":"Margalef Richness Index — index_margalef","title":"Margalef Richness Index — index_margalef","text":"Margalef Richness Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Margalef Richness Index — index_margalef","text":"","code":"index_margalef(x, ...) # S4 method for class 'numeric' index_margalef(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Margalef Richness Index — index_margalef","text":"x numeric vector count data (absolute frequencies). ... Currently used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Margalef Richness Index — index_margalef","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Margalef Richness Index — index_margalef","text":"Margalef, R. (1958). Information Theory Ecology. General Systems, 3, 36-71.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Margalef Richness Index — index_margalef","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":null,"dir":"Reference","previous_headings":"","what":"McIntosh Dominance Index. — index_mcintosh","title":"McIntosh Dominance Index. — index_mcintosh","text":"McIntosh Dominance Index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McIntosh Dominance Index. — index_mcintosh","text":"","code":"index_mcintosh(x, ...) # S4 method for class 'numeric' index_mcintosh(x, evenness = FALSE, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"McIntosh Dominance Index. — index_mcintosh","text":"x numeric vector count data (absolute frequencies). ... Currently used. evenness numeric scalar: evenness computed? na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"McIntosh Dominance Index. — index_mcintosh","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"McIntosh Dominance Index. — index_mcintosh","text":"McIntosh index expresses heterogeneity sample geometric terms. describes sample point \\(S\\)-dimensional hypervolume uses Euclidean distance point origin. dominance index, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"McIntosh Dominance Index. — index_mcintosh","text":"McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"McIntosh Dominance Index. — index_mcintosh","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":null,"dir":"Reference","previous_headings":"","what":"Menhinick Richness Index — index_menhinick","title":"Menhinick Richness Index — index_menhinick","text":"Menhinick Richness Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Menhinick Richness Index — index_menhinick","text":"","code":"index_menhinick(x, ...) # S4 method for class 'numeric' index_menhinick(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Menhinick Richness Index — index_menhinick","text":"x numeric vector count data (absolute frequencies). ... Currently used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Menhinick Richness Index — index_menhinick","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Menhinick Richness Index — index_menhinick","text":"Menhinick, E. F. (1964). Comparison Species-Individuals Diversity Indices Applied Samples Field Insects. Ecology, 45(4), 859-861. doi:10.2307/1934933 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Menhinick Richness Index — index_menhinick","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":null,"dir":"Reference","previous_headings":"","what":"Morisita-Horn Quantitative Index — index_morisita","title":"Morisita-Horn Quantitative Index — index_morisita","text":"Morisita-Horn Quantitative Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Morisita-Horn Quantitative Index — index_morisita","text":"","code":"index_morisita(x, y, ...) # S4 method for class 'numeric,numeric' index_morisita(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Morisita-Horn Quantitative Index — index_morisita","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Morisita-Horn Quantitative Index — index_morisita","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Morisita-Horn Quantitative Index — index_morisita","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Morisita-Horn Quantitative Index — index_morisita","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":null,"dir":"Reference","previous_headings":"","what":"Routledge Measures — index_routledge","title":"Routledge Measures — index_routledge","text":"Routledge Measures","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Routledge Measures — index_routledge","text":"","code":"index_routledge1(x, ...) index_routledge2(x, ...) index_routledge3(x, ...) # S4 method for class 'matrix' index_routledge1(x) # S4 method for class 'matrix' index_routledge2(x) # S4 method for class 'matrix' index_routledge3(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Routledge Measures — index_routledge","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Routledge Measures — index_routledge","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Routledge Measures — index_routledge","text":"assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Routledge Measures — index_routledge","text":"Routledge, R. D. (1977). Whittaker's Components Diversity. Ecology, 58(5), 1120-1127. doi:10.2307/1936932 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Routledge Measures — index_routledge","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":null,"dir":"Reference","previous_headings":"","what":"Shannon-Wiener Diversity Index — index_shannon","title":"Shannon-Wiener Diversity Index — index_shannon","text":"Shannon-Wiener Diversity Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shannon-Wiener Diversity Index — index_shannon","text":"","code":"index_shannon(x, ...) # S4 method for class 'numeric' index_shannon( x, evenness = FALSE, unbiased = FALSE, ACE = FALSE, base = exp(1), na.rm = FALSE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shannon-Wiener Diversity Index — index_shannon","text":"x numeric vector count data (absolute frequencies). ... Currently used. evenness numeric scalar: evenness computed? unbiased logical scalar: bias-corrected estimator used? ACE logical scalar: ACE species richness estimator used bias correction? base positive numeric value specifying base respect logarithms computed. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shannon-Wiener Diversity Index — index_shannon","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Shannon-Wiener Diversity Index — index_shannon","text":"Shannon index assumes individuals randomly sampled infinite population taxa represented sample (reflect sample size). main source error arises failure include taxa sample: error increases proportion species discovered sample declines (Peet 1974, Magurran 1988). maximum likelihood estimator (MLE) used relative abundance, known negatively biased sample size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Shannon-Wiener Diversity Index — index_shannon","text":"Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441 . Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Shannon, C. E. (1948). Mathematical Theory Communication. Bell System Technical Journal, 27, 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Shannon-Wiener Diversity Index — index_shannon","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":null,"dir":"Reference","previous_headings":"","what":"Simpson Dominance Index — index_simpson","title":"Simpson Dominance Index — index_simpson","text":"Simpson Dominance Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simpson Dominance Index — index_simpson","text":"","code":"index_simpson(x, ...) # S4 method for class 'numeric' index_simpson(x, evenness = FALSE, unbiased = FALSE, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simpson Dominance Index — index_simpson","text":"x numeric vector count data (absolute frequencies). ... Currently used. evenness numeric scalar: evenness computed? unbiased logical scalar: bias-corrected estimator used? na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simpson Dominance Index — index_simpson","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simpson Dominance Index — index_simpson","text":"Simpson index expresses probability two individuals randomly picked finite sample belong two different types. can interpreted weighted mean proportional abundances. metric true probability value, ranges \\(0\\) (taxa equally present) \\(1\\) (one taxon dominates community completely). dominance index, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simpson Dominance Index — index_simpson","text":"Simpson, E. H. (1949). Measurement Diversity. Nature, 163(4148), 688-688. doi:10.1038/163688a0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simpson Dominance Index — index_simpson","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorenson Qualitative Index — index_sorenson","title":"Sorenson Qualitative Index — index_sorenson","text":"Sorenson Qualitative Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorenson Qualitative Index — index_sorenson","text":"","code":"index_sorenson(x, y, ...) # S4 method for class 'logical,logical' index_sorenson(x, y) # S4 method for class 'numeric,numeric' index_sorenson(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorenson Qualitative Index — index_sorenson","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorenson Qualitative Index — index_sorenson","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sorenson Qualitative Index — index_sorenson","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sorenson Qualitative Index — index_sorenson","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":null,"dir":"Reference","previous_headings":"","what":"Squares Estimator — index_squares","title":"Squares Estimator — index_squares","text":"Squares Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Squares Estimator — index_squares","text":"","code":"index_squares(x, ...) # S4 method for class 'numeric' index_squares(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Squares Estimator — index_squares","text":"x numeric vector count data (absolute frequencies). ... Currently used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Squares Estimator — index_squares","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Squares Estimator — index_squares","text":"Alroy, J. (2018). Limits Species Richness Terrestrial Communities. Ecology Letters, 21(12), 1781-1789. doi:10.1111/ele.13152 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Squares Estimator — index_squares","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":null,"dir":"Reference","previous_headings":"","what":"Whittaker Measure — index_whittaker","title":"Whittaker Measure — index_whittaker","text":"Whittaker Measure","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Whittaker Measure — index_whittaker","text":"","code":"index_whittaker(x, ...) # S4 method for class 'matrix' index_whittaker(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Whittaker Measure — index_whittaker","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Whittaker Measure — index_whittaker","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Whittaker Measure — index_whittaker","text":"assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Whittaker Measure — index_whittaker","text":"Whittaker, R. H. (1960). Vegetation Siskiyou Mountains, Oregon California. Ecological Monographs, 30(3), 279-338. doi:10.2307/1943563 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Whittaker Measure — index_whittaker","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":null,"dir":"Reference","previous_headings":"","what":"Wilson Measure — index_wilson","title":"Wilson Measure — index_wilson","text":"Wilson Measure","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wilson Measure — index_wilson","text":"","code":"index_wilson(x, ...) # S4 method for class 'matrix' index_wilson(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wilson Measure — index_wilson","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wilson Measure — index_wilson","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wilson Measure — index_wilson","text":"assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wilson Measure — index_wilson","text":"Wilson, M. V., & Shmida, . (1984). Measuring Beta Diversity Presence-Absence Data. Journal Ecology, 72(3), 1055-1064. doi:10.2307/2259551 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Wilson Measure — index_wilson","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":null,"dir":"Reference","previous_headings":"","what":"Jackknife Estimation — jackknife","title":"Jackknife Estimation — jackknife","text":"Jackknife Estimation","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jackknife Estimation — jackknife","text":"","code":"# S4 method for class 'DiversityIndex' jackknife(object, f = NULL)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jackknife Estimation — jackknife","text":"object R object (typically DiversityIndex object). f function takes single numeric vector (leave-one-values ) argument.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jackknife Estimation — jackknife","text":"f NULL (default), jackknife() returns named numeric vector following elements: original observed value applied object. mean jackknife estimate mean . bias jackknife estimate bias . error jackknife estimate standard error . f function, jackknife() returns result f applied leave-one-values .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jackknife Estimation — jackknife","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jackknife Estimation — jackknife","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187 ## Jackknife resampling jackknife(h) #> original mean bias error #> Altamira 3.269200 3.246457 -0.9779158 0.08145529 #> Cueto de la Mina 2.955298 2.932856 -0.9649994 0.15907033 #> El Juyo 2.491683 2.469588 -0.9500688 0.19670002 #> El Cierro 2.485604 2.462723 -0.9838709 0.22195573 #> La Paloma 2.329187 2.305876 -1.0023704 0.25266291 jackknife(h, f = summary) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> Altamira 3.230811 3.236056 3.249552 3.246457 3.250979 3.269684 #> Cueto de la Mina 2.902193 2.911984 2.921893 2.932856 2.955298 3.018158 #> El Juyo 2.410614 2.444200 2.491683 2.469588 2.491683 2.529489 #> El Cierro 2.398698 2.425153 2.485604 2.462723 2.485604 2.485604 #> La Paloma 2.227438 2.260234 2.329187 2.305876 2.329187 2.329187"},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrigraph — matrigraph","title":"Matrigraph — matrigraph","text":"matrigraph() produces heatmap highlighting deviations independence. pvi() computes cell numeric matrix percentage column theoretical independence value.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrigraph — matrigraph","text":"","code":"matrigraph(object, ...) pvi(object, ...) # S4 method for class 'matrix' pvi(object) # S4 method for class 'data.frame' pvi(object) # S4 method for class 'matrix' matrigraph(object, reverse = FALSE, axes = TRUE, ...) # S4 method for class 'data.frame' matrigraph(object, reverse = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrigraph — matrigraph","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. reverse logical scalar: negative deviations centered (see details)? axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrigraph — matrigraph","text":"matrigraph() called side-effects: results graphic displayed (invisibly returns object). pvi() returns numeric matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Matrigraph — matrigraph","text":"PVI (french \"pourcentages de valeur d'indépendance\") calculated cell percentage column theoretical independence value: PVI greater \\(1\\) represent positive deviations independence, whereas PVI smaller \\(1\\) represent negative deviations (Desachy 2004). PVI matrix allows explore deviations independence (intuitive approach \\(\\chi^2\\)), way high-contrast matrix quite significant deviations, low risk due randomness (Desachy 2004). matrigraph() displays deviations independence: PVI equal \\(1\\) (statistical independence), cell matrix filled grey. PVI less \\(1\\) (negative deviation independence), size grey square proportional PVI (white margin thus represents fraction negative deviation). PVI greater \\(1\\) (positive deviation), black square representing fraction positive deviations superimposed. large positive deviations (PVI greater \\(2\\)), cell filled black. reverse TRUE, fraction negative deviations displayed white square.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Matrigraph — matrigraph","text":"Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Matrigraph — matrigraph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrigraph — matrigraph","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\") ## Matrigraph matrigraph(compiegne) matrigraph(compiegne, reverse = TRUE) ## Compute PVI counts_pvi <- pvi(compiegne) plot_heatmap(counts_pvi, col = khroma::color(\"iridescent\")(12))"},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object — mutators","title":"Get or Set Parts of an Object — mutators","text":"Getters setters extract replace parts object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or Set Parts of an Object — mutators","text":"","code":"get_method(x) # S4 method for class 'DiversityIndex' labels(object, ...) # S4 method for class 'RarefactionIndex' labels(object, ...) # S4 method for class 'DiversityIndex' get_method(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object — mutators","text":"object, x R object get set element(s). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get or Set Parts of an Object — mutators","text":"labels() returns suitable set labels object use printing plotting.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get or Set Parts of an Object — mutators","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Co-Occurrence — occurrence","title":"Co-Occurrence — occurrence","text":"Co-Occurrence","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Co-Occurrence — occurrence","text":"","code":"occurrence(object, ...) # S4 method for class 'matrix' occurrence(object) # S4 method for class 'data.frame' occurrence(object)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Co-Occurrence — occurrence","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Co-Occurrence — occurrence","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Co-Occurrence — occurrence","text":"co-occurrence matrix symmetric matrix zeros main diagonal, works many times pairs taxa/types occur together least one sample.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Co-Occurrence — occurrence","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Co-Occurrence — occurrence","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Plot spot diagram of a co-occurrence matrix occ <- occurrence(cantabria) plot_spot(occ)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bertin Diagram — plot_bertin","title":"Bertin Diagram — plot_bertin","text":"Plots Bertin diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bertin Diagram — plot_bertin","text":"","code":"plot_bertin(object, ...) # S4 method for class 'matrix' plot_bertin( object, threshold = NULL, freq = FALSE, margin = 1, col = c(\"white\", \"black\"), flip = TRUE, axes = TRUE, ... ) # S4 method for class 'data.frame' plot_bertin( object, threshold = NULL, freq = FALSE, margin = 1, col = c(\"white\", \"black\"), flip = TRUE, axes = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bertin Diagram — plot_bertin","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. threshold function takes numeric vector argument returns numeric threshold value (see ). NULL (default), threshold computed. used freq FALSE. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. col vector colors. flip logical scalar: x y axis flipped? Defaults TRUE. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bertin Diagram — plot_bertin","text":"plot_bertin() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"bertin-matrix","dir":"Reference","previous_headings":"","what":"Bertin Matrix","title":"Bertin Diagram — plot_bertin","text":"de Falguerolles et al. (1997) points : \"abstract terms, Bertin matrix matrix displays. ... fix ideas, think data matrix, variable case, real valued variables. variable, draw bar chart variable value case. High-light bars representing value sample threshold variable.\"","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bertin Diagram — plot_bertin","text":"Bertin, J. (1977). La graphique et le traitement graphique de l'information. Paris: Flammarion. Nouvelle Bibliothèque Scientifique. de Falguerolles, ., Friedrich, F. & Sawitzki, G. (1997). Tribute J. Bertin's Graphical Data Analysis. W. Badilla & F. Faulbaum (eds.), SoftStat '97: Advances Statistical Software 6. Stuttgart: Lucius & Lucius, p. 11-20.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bertin Diagram — plot_bertin","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bertin Diagram — plot_bertin","text":"","code":"## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\") ## Plot a Bertin diagram... ## ...without threshold plot_bertin(mississippi) ## ...with the variable mean as threshold plot_bertin(mississippi, threshold = mean) ## Plot conditional proportions plot_bertin(mississippi, freq = TRUE, margin = 1) plot_bertin(mississippi, freq = TRUE, margin = 2)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":null,"dir":"Reference","previous_headings":"","what":"Dice-Leraas Diagram — plot_diceleraas","title":"Dice-Leraas Diagram — plot_diceleraas","text":"Plots Dice-Leraas diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dice-Leraas Diagram — plot_diceleraas","text":"","code":"plot_diceleraas(object, ...) # S4 method for class 'matrix' plot_diceleraas( object, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = FALSE, panel.first = NULL, panel.last = NULL, ... ) # S4 method for class 'data.frame' plot_diceleraas( object, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = FALSE, panel.first = NULL, panel.last = NULL, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dice-Leraas Diagram — plot_diceleraas","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dice-Leraas Diagram — plot_diceleraas","text":"plot_diceleraas() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dice-Leraas Diagram — plot_diceleraas","text":"Dice-Leraas diagram, horizontal line represents range data (min-max) small vertical line indicates mean. black rectangle twice standard error mean, white rectangle one standard deviation either side mean.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dice-Leraas Diagram — plot_diceleraas","text":"Dice, L. R., & Leraas, H. J. (1936). Graphic Method Comparing Several Sets Measurements. Contributions Laboratory Vertebrate Genetics, 3: 1-3. Hubbs, C. L., & C. Hubbs (1953). Improved Graphical Analysis Comparison Series Samples. Systematic Biology, 2(2): 49-56. doi:10.2307/sysbio/2.2.49 . Simpson, G. G., Roe, ., & Lewontin, R. C. Quantitative Zoology. New York: Harcourt, Brace Company, 1960.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dice-Leraas Diagram — plot_diceleraas","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dice-Leraas Diagram — plot_diceleraas","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\") ## Plot a Dice-Leraas diagram plot_diceleraas(compiegne)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Plot — plot_diversity","title":"Diversity Plot — plot_diversity","text":"Diversity Plot","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Plot — plot_diversity","text":"","code":"# S4 method for class 'DiversityIndex,missing' plot( x, log = \"x\", col.mean = \"#DDAA33\", col.interval = \"#004488\", lty.mean = \"solid\", lty.interval = \"dashed\", lwd.mean = 1, lwd.interval = 1, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Plot — plot_diversity","text":"x DiversityIndex object plotted. log character string indicating axes log scale. Defaults x. col.mean, col.interval character string specifying color lines. lty.mean, lty.interval character string numeric value specifying line types. lwd.mean, lwd.interval non-negative numeric value specifying line widths. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters passed graphics::points(), particularly, cex, col pch.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Plot — plot_diversity","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Plot — plot_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Plot — plot_diversity","text":"","code":"# \\donttest{ ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = \"shannon\") h_sim <- simulate(h) plot(h_sim) r <- richness(cantabria, method = \"count\") r_sim <- simulate(r) plot(r_sim) # }"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":null,"dir":"Reference","previous_headings":"","what":"Ford Diagram — plot_ford","title":"Ford Diagram — plot_ford","text":"Plots Ford (battleship curve) diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ford Diagram — plot_ford","text":"","code":"plot_ford(object, ...) # S4 method for class 'matrix' plot_ford( object, weights = FALSE, EPPM = FALSE, fill = \"darkgrey\", border = NA, axes = TRUE, ... ) # S4 method for class 'data.frame' plot_ford( object, weights = FALSE, EPPM = FALSE, fill = \"darkgrey\", border = NA, axes = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ford Diagram — plot_ford","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. weights logical scalar: row sums displayed? EPPM logical scalar: EPPM drawn? See seriograph(). fill color filling bars. border color draw borders. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ford Diagram — plot_ford","text":"plot_ford() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ford Diagram — plot_ford","text":"Ford, J. . (1962). quantitative method deriving cultural chronology. Washington, DC: Pan American Union. Technical manual 1.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ford Diagram — plot_ford","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ford Diagram — plot_ford","text":"","code":"## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\") ## Plot a Ford diagram plot_ford(mississippi) plot_ford(mississippi, weights = TRUE)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap — plot_heatmap","title":"Heatmap — plot_heatmap","text":"Plots heatmap.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap — plot_heatmap","text":"","code":"plot_heatmap(object, ...) # S4 method for class 'matrix' plot_heatmap( object, col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE), diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, fixed_ratio = TRUE, axes = TRUE, legend = TRUE, ... ) # S4 method for class 'data.frame' plot_heatmap( object, col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE), diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, fixed_ratio = TRUE, axes = TRUE, legend = TRUE, ... ) # S4 method for class 'dist' plot_heatmap( object, col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE), diag = FALSE, upper = FALSE, lower = !upper, axes = TRUE, legend = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap — plot_heatmap","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. col vector colors. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. fixed_ratio logical scalar: fixed aspect ratio (1) used? axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap — plot_heatmap","text":"plot_heatmap() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heatmap — plot_heatmap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap — plot_heatmap","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Plot raw data plot_heatmap(cantabria) ## Plot conditional proportions plot_heatmap(cantabria, freq = TRUE, margin = 1) plot_heatmap(cantabria, freq = TRUE, margin = 2)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix Plot — plot_matrix","title":"Matrix Plot — plot_matrix","text":"Matrix Plot","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix Plot — plot_matrix","text":"","code":"plot_matrix( object, panel, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, scale = TRUE, drop_zero = TRUE, col = graphics::par(\"fg\"), midpoint = NULL, axes = TRUE, legend = TRUE, asp = 1, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix Plot — plot_matrix","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). panel function form function(x, y, z, color, ...) gives action carried panel display. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. scale logical scalar indicating whether data rescaled \\([-1,1]\\). used freq FALSE. drop_zero logical scalar: zeros discarded? col vector colors. midpoint numeric value specifying data midpoint. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed? asp length-one numeric vector, giving aspect ratio \\(y/x\\). ... arguments passed panel.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Rank Plot — plot_rank","title":"Rank Plot — plot_rank","text":"Plots rank vs relative abundance diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rank Plot — plot_rank","text":"","code":"plot_rank(object, ...) # S4 method for class 'matrix' plot_rank( object, log = NULL, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topright\"), ... ) # S4 method for class 'data.frame' plot_rank( object, log = NULL, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topright\"), ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rank Plot — plot_rank","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters. log character string contains \"x\" x axis logarithmic, \"y\" y axis logarithmic \"xy\" \"yx\" axes logarithmic (base 10). main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rank Plot — plot_rank","text":"plot_rank() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rank Plot — plot_rank","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rank Plot — plot_rank","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rank Plot — plot_rank","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Plot rank vs abundance plot_rank(cantabria) ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot_rank(cantabria, col = col, pch = 15:19, lty = 2)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Plot — plot_rarefaction","title":"Rarefaction Plot — plot_rarefaction","text":"Rarefaction Plot","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction Plot — plot_rarefaction","text":"","code":"# S4 method for class 'RarefactionIndex,missing' plot( x, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topleft\"), ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction Plot — plot_rarefaction","text":"x RarefactionIndex object plotted. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed. ... graphical parameters passed graphics::lines().","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction Plot — plot_rarefaction","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Plot — plot_rarefaction","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction Plot — plot_rarefaction","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = \"baxter\") plot(rare, panel.first = graphics::grid()) ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot(rare, col = col, lty = 1:5)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":null,"dir":"Reference","previous_headings":"","what":"Spot Plot — plot_spot","title":"Spot Plot — plot_spot","text":"Plots spot matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spot Plot — plot_spot","text":"","code":"plot_spot(object, ...) # S4 method for class 'matrix' plot_spot( object, type = c(\"ring\", \"plain\"), col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE), diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, axes = TRUE, legend = TRUE, ... ) # S4 method for class 'data.frame' plot_spot( object, type = c(\"ring\", \"plain\"), col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE), diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, axes = TRUE, legend = TRUE, ... ) # S4 method for class 'dist' plot_spot( object, type = c(\"ring\", \"plain\"), col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE), diag = FALSE, upper = FALSE, lower = !upper, axes = TRUE, legend = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spot Plot — plot_spot","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. type character string specifying graph plotted. must one \"ring\" (default) \"plain\". unambiguous substring can given. col vector colors. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spot Plot — plot_spot","text":"plot_spot() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spot Plot — plot_spot","text":"spot matrix can considered variant Bertin diagram data first transformed relative frequencies.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Spot Plot — plot_spot","text":"Adapted Dan Gopstein's original idea.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spot Plot — plot_spot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spot Plot — plot_spot","text":"","code":"## Data from Huntley 2004, 2008 data(\"pueblo\") ## Plot spot diagram of count data plot_spot(pueblo, type = \"ring\") plot_spot(pueblo, type = \"plain\") ## Plot conditional proportions plot_spot(pueblo, freq = TRUE, margin = 1) plot_spot(pueblo, freq = TRUE, margin = 2)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Profiles — profiles","title":"Diversity Profiles — profiles","text":"Diversity Profiles","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Profiles — profiles","text":"","code":"profiles(object, ...) # S4 method for class 'matrix' profiles( object, alpha = seq(from = 0, to = 4, by = 0.04), main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topright\"), ... ) # S4 method for class 'data.frame' profiles( object, alpha = seq(from = 0, to = 4, by = 0.04), main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topright\"), ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Profiles — profiles","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters passed graphics::lines() alpha numeric vector giving values alpha parameter. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Profiles — profiles","text":"profiles() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diversity Profiles — profiles","text":"profiles cross, diversities non-comparable across samples.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diversity Profiles — profiles","text":"Tóthmérész, B. (1995). Comparison Different Methods Diversity Ordering. Journal Vegetation Science, 6(2), 283-290. doi:10.2307/3236223 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Profiles — profiles","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Profiles — profiles","text":"","code":"## Replicate fig. 1 of Tóthmérész 1995 spc <- matrix( data = c(33, 29, 28, 5, 5, 0, 0, 42, 30, 10, 8, 5, 5, 0, 32, 21, 16, 12, 9, 6, 4), nrow = 3, byrow = TRUE, dimnames = list(c(\"A\", \"B\", \"C\"), NULL) ) profiles(spc)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/pueblo.html","id":null,"dir":"Reference","previous_headings":"","what":"Pueblo IV Period Ceramics — pueblo","title":"Pueblo IV Period Ceramics — pueblo","text":"dataset ceramic counts Zuni region.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/pueblo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pueblo IV Period Ceramics — pueblo","text":"","code":"pueblo"},{"path":"https://packages.tesselle.org/tabula/dev/reference/pueblo.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pueblo IV Period Ceramics — pueblo","text":"data.frame 9 rows 5 variables (compositional groups).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/pueblo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Pueblo IV Period Ceramics — pueblo","text":"Huntley, D. L. (2004). Interaction, Boundaries, Identities: Multiscalar Approach Organizational Scale Pueblo IV Zuni Society. Ph.D. Dissertation, Arizona State University. Huntley, D. L. (2022). Ancestral Zuni Glaze-Decorated Pottery: Viewing Pueblo IV Regional Organization Ceramic Production Exchange. Anthropological Papers University Arizona 72. Tucson: University Arizona Press. doi:10.2307/j.ctv2ngx5n8 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction — rarefaction","title":"Rarefaction — rarefaction","text":"Rarefaction","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction — rarefaction","text":"","code":"rarefaction(object, ...) # S4 method for class 'matrix' rarefaction(object, sample = NULL, method = c(\"hurlbert\", \"baxter\"), step = 1) # S4 method for class 'data.frame' rarefaction(object, sample = NULL, method = c(\"hurlbert\", \"baxter\"), step = 1)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction — rarefaction","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. sample length-one numeric vector giving sub-sample size. size sample smaller total community size. method character string vector strings specifying index computed (see details). unambiguous substring can given. step integer giving increment sample size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction — rarefaction","text":"RarefactionIndex object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"rarefaction-measures","dir":"Reference","previous_headings":"","what":"Rarefaction Measures","title":"Rarefaction — rarefaction","text":"following rarefaction measures available count data: baxter Baxter's rarefaction. hurlbert Hurlbert's unbiased estimate Sander's rarefaction.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rarefaction — rarefaction","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (\\(S\\)) can used concept taxa number (McIntosh 1967). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(E(S)\\)) number taxa expected samples standard size (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction — rarefaction","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction — rarefaction","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = \"baxter\") plot(rare, panel.first = graphics::grid()) ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot(rare, col = col, lty = 1:5)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. arkhe remove_NA, remove_constant, remove_zero, replace_NA, replace_zero, sparsity","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample — resample","title":"Resample — resample","text":"Simulates observations multinomial distribution.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample — resample","text":"","code":"resample(object, ...) # S4 method for class 'numeric' resample(object, do, n, size = sum(object), ..., f = NULL)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample — resample","text":"object numeric vector count data (absolute frequencies). ... Extra arguments passed . function takes object argument returns single numeric value. n non-negative integer specifying number bootstrap replications. size non-negative integer specifying sample size. f function takes single numeric vector (result ) argument.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample — resample","text":"f NULL, resample() returns n values . Else, returns result f applied n values .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample — resample","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resample — resample","text":"","code":"## Sample observations from a multinomial distribution x <- sample(1:100, 50, TRUE) resample(x, do = median, n = 100) #> [1] 49.0 52.0 52.0 50.5 50.5 51.5 50.0 49.5 50.0 49.0 45.5 49.5 53.0 50.5 51.0 #> [16] 50.0 49.5 50.0 48.5 48.5 45.5 50.5 54.0 48.5 46.5 50.0 46.5 49.0 50.0 52.0 #> [31] 48.0 46.0 50.0 48.5 52.0 50.0 49.5 50.5 50.0 49.5 54.0 49.5 50.0 52.5 49.5 #> [46] 51.5 47.5 51.0 53.5 53.0 52.0 52.5 50.0 47.0 48.5 48.0 51.0 50.5 48.0 54.0 #> [61] 51.0 48.0 49.5 49.5 48.5 49.5 53.5 50.0 53.0 51.5 50.5 52.5 54.0 49.5 46.5 #> [76] 47.5 50.5 49.0 47.5 47.5 51.0 49.0 46.5 47.0 51.0 51.0 48.5 52.0 50.0 49.5 #> [91] 52.5 51.0 51.0 49.0 51.0 46.0 50.0 50.0 50.5 51.5 ## Estimate the 25th, 50th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.50, 0.75)) } resample(x, n = 100, do = median, f = quant) #> 25% 50% 75% #> 48.5 50.0 51.5"},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness — richness","title":"Richness — richness","text":"richness() computes sample richness. composition() computes asymptotic species richness.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Richness — richness","text":"","code":"richness(object, ...) composition(object, ...) # S4 method for class 'matrix' richness(object, ..., method = c(\"count\", \"margalef\", \"menhinick\")) # S4 method for class 'data.frame' richness(object, ..., method = c(\"count\", \"margalef\", \"menhinick\")) # S4 method for class 'matrix' composition(object, ..., method = c(\"chao1\", \"ace\", \"squares\", \"chao2\", \"ice\")) # S4 method for class 'data.frame' composition(object, ..., method = c(\"chao1\", \"ace\", \"squares\", \"chao2\", \"ice\"))"},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Richness — richness","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods (see ). method character string vector strings specifying index computed (see details). unambiguous substring can given.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Richness — richness","text":"richness() returns RichnessIndex object. composition() returns CompositionIndex object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Richness — richness","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (\\(S\\)) can used concept taxa number (McIntosh 1967). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(E(S)\\)) number taxa expected samples standard size (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"richness-measures","dir":"Reference","previous_headings":"","what":"Richness Measures","title":"Richness — richness","text":"following richness measures available count data: count Number observed taxa/types. margalef Margalef richness index. menhinick Menhinick richness index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"asymptotic-species-richness","dir":"Reference","previous_headings":"","what":"Asymptotic Species Richness","title":"Richness — richness","text":"following measures available count data: ace Abundance-based Coverage Estimator. chao1 (improved/unbiased) Chao1 estimator. squares Squares estimator. following measures available replicated incidence data: ice Incidence-based Coverage Estimator. chao2 (improved/unbiased) Chao2 estimator.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Richness — richness","text":"Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36. Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Magurran, E. & Brian J. McGill (2011). Biological Diversity: Frontiers Measurement Assessment. Oxford: Oxford University Press. McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674 . Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness — richness","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Richness — richness","text":"","code":"## Data from Magurran 1988, p. 128-129 trap <- matrix(data = c(9, 3, 0, 4, 2, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 2, 0, 5, 3, 0), nrow = 2, byrow = TRUE, dimnames = list(c(\"A\", \"B\"), NULL)) ## Margalef and Menhinick index richness(trap, method = \"margalef\") # 2.55 1.88 #> [1] 2.551432 1.949356 richness(trap, method = \"menhinick\") # 1.95 1.66 #> [1] 1.876630 1.664101 ## Data from Chao & Chiu 2016 brazil <- matrix( data = rep(x = c(1:21, 23, 25, 27, 28, 30, 32, 34:37, 41, 45, 46, 49, 52, 89, 110, 123, 140), times = c(113, 50, 39, 29, 15, 11, 13, 5, 6, 6, 3, 4, 3, 5, 2, 5, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0)), nrow = 1, byrow = TRUE ) ## Chao1-type estimators (asymptotic species richness) composition(brazil, method = c(\"chao1\"), unbiased = FALSE) # 461.625 #> [1] 461.6254 composition(brazil, method = c(\"ace\"), k = 10) # 445.822 #> [1] 443.6836"},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Seriograph — seriograph","title":"Seriograph — seriograph","text":"seriograph() produces Ford diagram highlighting relationships rows columns. eppm() computes cell numeric matrix positive difference column mean percentage.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seriograph — seriograph","text":"","code":"seriograph(object, ...) eppm(object, ...) # S4 method for class 'matrix' eppm(object) # S4 method for class 'data.frame' eppm(object) # S4 method for class 'matrix' seriograph( object, weights = FALSE, fill = \"darkgrey\", border = NA, axes = TRUE, ... ) # S4 method for class 'data.frame' seriograph( object, weights = FALSE, fill = \"darkgrey\", border = NA, axes = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seriograph — seriograph","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. weights logical scalar: row sums displayed? fill color filling bars. border color draw borders. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seriograph — seriograph","text":"seriograph() called side-effects: results graphic displayed (invisibly returns object). eppm() returns numeric matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Seriograph — seriograph","text":"positive difference column mean percentage (french \"écart positif au pourcentage moyen\", EPPM) represents deviation situation statistical independence. independence can interpreted absence relationships types chronological order assemblages, EPPM useful tool explore significance relationship rows columns related seriation (Desachy 2004). seriograph() superimposes frequencies (grey) EPPM values (black) row-column pair Ford diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Seriograph — seriograph","text":"Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Seriograph — seriograph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seriograph — seriograph","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\") ## Seriograph seriograph(compiegne) seriograph(compiegne, weights = TRUE) ## Compute EPPM counts_eppm <- eppm(compiegne) plot_heatmap(counts_eppm, col = khroma::color(\"YlOrBr\")(12))"},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":null,"dir":"Reference","previous_headings":"","what":"SHE Analysis — she","title":"SHE Analysis — she","text":"Analysis","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHE Analysis — she","text":"","code":"she(object, ...) # S4 method for class 'matrix' she( object, unbiased = FALSE, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"right\"), ... ) # S4 method for class 'data.frame' she( object, unbiased = FALSE, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"right\"), ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHE Analysis — she","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters passed graphics::lines() graphics::points(). unbiased logical scalar: bias-corrected estimator used (see index_shannon())? main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHE Analysis — she","text":"() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHE Analysis — she","text":"samples taken along gradient stratigraphic section, breaks curve may used infer discontinuities. assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHE Analysis — she","text":"Buzas, M. . & Hayek, L.-. C. (1998). analysis biofacies identification. Journal Foraminiferal Research, 1998, 28(3), 233-239. Hayek, L.-. C. & Buzas, M. . (2010). Surveying Natural Populations: Quantitative Tools Assessing Biodiversity. Second edition. New York: Columbia University Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"SHE Analysis — she","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHE Analysis — she","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## SHE analysis she(cantabria)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Similarity — similarity","title":"Similarity — similarity","text":"Similarity","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Similarity — similarity","text":"","code":"similarity(object, ...) # S4 method for class 'matrix' similarity( object, method = c(\"brainerd\", \"bray\", \"jaccard\", \"morisita\", \"sorenson\", \"binomial\") ) # S4 method for class 'data.frame' similarity( object, method = c(\"brainerd\", \"bray\", \"jaccard\", \"morisita\", \"sorenson\", \"binomial\") )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Similarity — similarity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. method character string specifying method used (see details). unambiguous substring can given.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Similarity — similarity","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Similarity — similarity","text":"\\(\\beta\\)-diversity can measured addressing similarity pairs samples/cases (Brainerd-Robinson, Jaccard, Morisita-Horn Sorenson indices). Similarity pairs taxa/types can measured assessing degree co-occurrence (binomial co-occurrence). Jaccard, Morisita-Horn Sorenson indices provide scale similarity \\(0\\)-\\(1\\) \\(1\\) perfect similarity \\(0\\) similarity. Brainerd-Robinson index scaled \\(0\\) \\(200\\). Binomial co-occurrence assessment approximates Z-score. binomial Binomial co-occurrence assessment. brainerd Brainerd-Robinson quantitative index. bray Sorenson quantitative index. jaccard Jaccard qualitative index. morisita Morisita-Horn quantitative index. sorenson Sorenson qualitative index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Similarity — similarity","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Similarity — similarity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Similarity — similarity","text":"","code":"## Data from Huntley 2004, 2008 data(\"pueblo\") ## Brainerd-Robinson measure (C <- similarity(pueblo, \"brainerd\")) #> Atsinna Cienega Mirabal PdMuertos Hesh LowPesc BoxS #> Cienega 164.36782 #> Mirabal 152.38095 138.09524 #> PdMuertos 179.31034 150.57471 152.38095 #> Hesh 82.75862 55.55556 66.66667 103.44828 #> LowPesc 89.21023 62.00717 73.11828 109.89989 193.54839 #> BoxS 82.75862 55.55556 114.28571 102.17114 103.70370 103.70370 #> Ojo Bon 27.58621 22.22222 19.04762 20.68966 26.66667 25.80645 22.22222 #> S170 27.58621 22.22222 19.04762 20.68966 26.66667 25.80645 22.22222 #> Ojo Bon #> Cienega #> Mirabal #> PdMuertos #> Hesh #> LowPesc #> BoxS #> Ojo Bon #> S170 190.53030 plot_spot(C) ## Data from Magurran 1988, p. 166 data(\"aves\") ## Jaccard measure (presence/absence data) similarity(aves, \"jaccard\") # 0.46 #> unmanaged #> managed 0.4615385 ## Sorenson measure (presence/absence data) similarity(aves, \"sorenson\") # 0.63 #> unmanaged #> managed 0.6315789 # Jaccard measure (Bray's formula ; count data) similarity(aves, \"bray\") # 0.44 #> unmanaged #> managed 0.4442754 # Morisita-Horn measure (count data) similarity(aves, \"morisita\") # 0.81 #> unmanaged #> managed 0.8134497"},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Measure Diversity by Comparing to Simulated Assemblages — simulate","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"Measure Diversity Comparing Simulated Assemblages","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"","code":"# S4 method for class 'DiversityIndex' simulate( object, n = 1000, step = 1, interval = c(\"percentiles\", \"student\", \"normal\"), level = 0.8, progress = getOption(\"tabula.progress\") )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"object DiversityIndex object. n non-negative integer giving number bootstrap replications. step integer giving increment sample size. interval character string giving type confidence interval returned. must one \"percentiles\" (sample quantiles, described Kintigh 1984; default), \"student\" \"normal\". unambiguous substring can given. level length-one numeric vector giving confidence level. progress logical scalar: progress bar displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"Returns DiversityIndex object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184 . Kintigh, K. W. (1984). Measuring Archaeological Diversity Comparison Simulated Assemblages. American Antiquity, 49(1), 44-54. doi:10.2307/280511 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"","code":"# \\donttest{ ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = \"shannon\") h_sim <- simulate(h) plot(h_sim) r <- richness(cantabria, method = \"count\") r_sim <- simulate(r) plot(r_sim) # }"},{"path":"https://packages.tesselle.org/tabula/dev/reference/tabula-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in tabula — tabula-deprecated","title":"Deprecated Functions in tabula — tabula-deprecated","text":"functions still work removed (defunct) next version.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/tabula-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions in tabula — tabula-deprecated","text":"","code":"test_diversity(object, ...) # S4 method for class 'matrix' test_diversity(object, adjust = \"holm\", ...) # S4 method for class 'data.frame' test_diversity(object, adjust = \"holm\", ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/tabula-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tabula: Analysis and Visualization of Archaeological Count Data — tabula-package","title":"tabula: Analysis and Visualization of Archaeological Count Data — tabula-package","text":"easy way examine archaeological count data. package provides several tests measures diversity: heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). allows easily visualize count data statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) Bertin (1977) diagrams, etc.","code":""},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/tabula-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tabula: Analysis and Visualization of Archaeological Count Data — tabula-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Archéosciences Bordeaux (UMR 6034) Maison de l'Archéologie Université Bordeaux Montaigne 33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Test — test","title":"Diversity Test — test","text":"Compares Shannon/Simpson diversity samples.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Test — test","text":"","code":"test_shannon(x, y, ...) test_simpson(x, y, ...) # S4 method for class 'numeric,numeric' test_shannon(x, y, ...) # S4 method for class 'matrix,missing' test_shannon(x, adjust = \"holm\", ...) # S4 method for class 'data.frame,missing' test_shannon(x, adjust = \"holm\", ...) # S4 method for class 'numeric,numeric' test_simpson(x, y, adjust = \"holm\", ...) # S4 method for class 'matrix,missing' test_simpson(x, adjust = \"holm\", ...) # S4 method for class 'data.frame,missing' test_simpson(x, adjust = \"holm\", ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Test — test","text":"x, y numeric vector, \\(m \\times p\\) matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. adjust character string specifying method adjusting \\(p\\) values (see stats::p.adjust()).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Test — test","text":"x y numeric vectors, returns list containing following components: statistic value t-statistic. parameter degrees freedom t-statistic. p.value p-value test. x matrix data.frame, returns table adjusted p-values lower triangular form.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Diversity Test — test","text":"test_shannon(x = matrix, y = missing): Produces two sided pairwise comparisons. test_shannon(x = data.frame, y = missing): Produces two sided pairwise comparisons. test_simpson(x = matrix, y = missing): Produces two sided pairwise comparisons. test_simpson(x = data.frame, y = missing): Produces two sided pairwise comparisons.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diversity Test — test","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Test — test","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Test — test","text":"","code":"## Data from Magurran 1988, p. 145-149 oakwood <- c(35, 26, 25, 21, 16, 11, 6, 5, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 0, 0) spruce <- c(30, 30, 3, 65, 20, 11, 0, 4, 2, 14, 0, 3, 9, 0, 0, 5, 0, 0, 0, 0, 1, 1) test_shannon(oakwood, spruce) #> $statistic #> [1] 3.534228 #> #> $parameter #> [1] 358.1946 #> #> $p.value #> [1] 0.0004625638 #> test_simpson(oakwood, spruce) #> $statistic #> [1] -2.871559 #> #> $parameter #> [1] 324.5595 #> #> $p.value #> [1] 0.004353068 #> ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") test_shannon(cantabria) #> Altamira Cueto de la Mina El Juyo El Cierro #> Cueto de la Mina 8.268025e-02 NA NA NA #> El Juyo 3.754291e-05 0.06016855 NA NA #> El Cierro 3.526160e-05 0.05708787 1 NA #> La Paloma 3.255839e-05 0.01313679 1 1 test_simpson(cantabria) #> Altamira Cueto de la Mina El Juyo El Cierro #> Cueto de la Mina 0.84391773 NA NA NA #> El Juyo 0.05934092 0.6404647 NA NA #> El Cierro 0.32909278 1.0000000 1 NA #> La Paloma 0.37092662 1.0000000 1 1"},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":null,"dir":"Reference","previous_headings":"","what":"Turnover — turnover","title":"Turnover — turnover","text":"Returns degree turnover taxa composition along gradient transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turnover — turnover","text":"","code":"turnover(object, ...) # S4 method for class 'matrix' turnover( object, ..., method = c(\"whittaker\", \"cody\", \"routledge1\", \"routledge2\", \"routledge3\", \"wilson\") ) # S4 method for class 'data.frame' turnover( object, ..., method = c(\"whittaker\", \"cody\", \"routledge1\", \"routledge2\", \"routledge3\", \"wilson\") )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turnover — turnover","text":"object \\(m \\times p\\) numeric matrix data.frame count data incidence data. data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. method character string specifying method used (see details). unambiguous substring can given.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turnover — turnover","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Turnover — turnover","text":"following methods can used ascertain degree turnover taxa composition along gradient (\\(\\beta\\)-diversity) qualitative (presence/absence) data: cody Cody measure. routledge1 Routledge first measure. routledge2 Routledge second measure. routledge3 Routledge third measure (exponential form second measure). whittaker Whittaker measure. wilson Wilson measure. assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Turnover — turnover","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turnover — turnover","text":"","code":"## Data from Magurran 1988, p. 162 data(\"woodland\") ## Whittaker's measure turnover(woodland, \"whittaker\") # 1 #> [1] 1 ## Cody's measure turnover(woodland, \"cody\") # 3 #> [1] 1 ## Routledge's measures turnover(woodland, \"routledge1\") # 0.29 #> [1] 1 turnover(woodland, \"routledge2\") # 0.56 #> [1] 1 turnover(woodland, \"routledge3\") # 1.75 #> [1] 1 ## Wilson and Shmida's measure turnover(woodland, \"wilson\") # 1 #> [1] 1"},{"path":"https://packages.tesselle.org/tabula/dev/reference/woodland.html","id":null,"dir":"Reference","previous_headings":"","what":"Trees Incidences — woodland","title":"Trees Incidences — woodland","text":"dataset presence absence trees six (10 x 10 m) quadarts along transect deciduous woodland.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/woodland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trees Incidences — woodland","text":"","code":"woodland"},{"path":"https://packages.tesselle.org/tabula/dev/reference/woodland.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Trees Incidences — woodland","text":"data.frame 6 rows (quadarts) 6 variables (tree species).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/woodland.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Trees Incidences — woodland","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]}] +[{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"shannon-wiener-diversity-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Information theory index","what":"Shannon-Wiener diversity index","title":"alpha Diversity","text":"Shannon-Wiener index (Shannon 1948) assumes individuals randomly sampled infinite population taxa represented sample (reflect sample size). main source error arises failure include taxa sample: error increases proportion species discovered sample declines (Peet 1974; Magurran 1988). Heterogeneity finite sample: H=−∑=1Sp̂ilnp̂H = - \\sum_{= 1}^{S} \\hat{p}_i \\ln \\hat{p}_i used maximum likelihood estimator (MLE) known negatively biased sample size (error rarely significant; Peet 1974). bias correction (unbiased TRUE): H=−∑=1Sp̂ilnp̂+S−12n H = - \\sum_{= 1}^{S} \\hat{p}_i \\ln \\hat{p}_i + \\frac{S - 1}{2n} Evenness: E=HlnS=−∑=1Sp̂ilogSp̂E = \\frac{H}{\\ln S} = - \\sum_{= 1}^{S} \\hat{p}_i \\log_S \\hat{p}_i","code":"index_shannon(woodland) #> [1] 2.407983 index_shannon(woodland, unbiased = TRUE) #> [1] 2.463865 index_shannon(woodland, evenness = TRUE) #> [1] 0.8038044"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"brillouin-diversity-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Information theory index","what":"Brillouin diversity index","title":"alpha Diversity","text":"Brillouin index (Brillouin 1956) describes known collection: assume random sampling infinite population. Pielou (1975) Laxton (1978) argue use Brillouin index circumstances, especially preference Shannon index. Heterogeneity: H=ln(n!)−∑=1Sln(ni!)n H = \\frac{\\ln (n!) - \\sum_{= 1}^{S} \\ln (n_i!)}{n} Evenness: E=HHmax E = \\frac{H}{H_{max}} : Hmax=1nlnn!(⌊nS⌋!)S−r[(⌊nS⌋+1)!]r H_{max} = \\frac{1}{n} \\ln \\frac{n!}{\\left( \\lfloor \\frac{n}{S} \\rfloor! \\right)^{S - r} \\left[ \\left( \\lfloor \\frac{n}{S} \\rfloor + 1 \\right)! \\right]^{r}} : r=n−S⌊nS⌋r = n - S \\lfloor \\frac{n}{S} \\rfloor.","code":"index_brillouin(woodland) #> [1] 2.230661 index_brillouin(woodland, evenness = TRUE) #> [1] 0.8025508"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"dominance-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness","what":"Dominance index","title":"alpha Diversity","text":"following methods return dominance index, reciprocal inverse form usually adopted, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"simpson-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Dominance index","what":"Simpson index","title":"alpha Diversity","text":"Simpson index (Simpson 1949) expresses probability two individuals randomly picked finite sample belong two different types. can interpreted weighted mean proportional abundances. metric true probability value, ranges 00 (taxa equally present) 11 (one taxon dominates community completely). Dominance infinite sample: D=∑=1Spi2 D = \\sum_{= 1}^{S} p_i^2 Dominance finite sample: D=∑=1Sni(ni−1)n(n−1) D = \\sum_{= 1}^{S} \\frac{n_i \\left( n_i - 1 \\right)}{n \\left( n - 1 \\right)}","code":"index_simpson(woodland) #> [1] 0.1199308 index_simpson(woodland, evenness = TRUE) #> [1] 0.4169071"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"mcintosh-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Dominance index","what":"McIntosh index","title":"alpha Diversity","text":"McIntosh index (McIntosh 1967) expresses heterogeneity sample geometric terms. describes sample point SS-dimensional hypervolume uses Euclidean distance point origin. Dominance: D=n−Un−n D = \\frac{n - U}{n - \\sqrt{n}} Evenness: E=n−Un−nS E = \\frac{n - U}{n - \\frac{n}{\\sqrt{S}}} UU distance sample origin SS dimensional hypervolume: U=∑=1Sni2U = \\sqrt{\\sum_{= 1}^{S} n_i^2}","code":"index_mcintosh(woodland) #> [1] 0.7079901 index_mcintosh(woodland, evenness = TRUE) #> [1] 0.8419571"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"berger-parker-index","dir":"Articles","previous_headings":"Heterogeneity and Evenness > Dominance index","what":"Berger-Parker index","title":"alpha Diversity","text":"Berger-Parker index (Berger & Parker 1970) expresses proportional importance abundant type. metric highly biased sample size richness, moreover make use information available sample. Dominance: D=nmaxN D = \\frac{n_{max}}{N}","code":"index_berger(woodland) #> [1] 0.2058824"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"richness","dir":"Articles","previous_headings":"","what":"Richness","title":"alpha Diversity","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (RR) can used concept taxa number (McIntosh 1967). Richness refers variety taxa/species/types present assemblage community (Bobrowsky & Ball 1989) “number species present collection containing specified number individuals” (Hurlbert 1971). Richness measures (Bobrowsky & Ball 1989).","code":"index_margalef(woodland) #> [1] 3.699522 index_menhinick(woodland) #> [1] 1.53393"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"asymptotic-species-richness","dir":"Articles","previous_headings":"Richness","what":"Asymptotic Species Richness","title":"alpha Diversity","text":"Estimators asymptotic richness based frequencies rare species original sampling data.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"chao1-estimator-chao-1984","dir":"Articles","previous_headings":"Richness > Asymptotic Species Richness","what":"Chao1 estimator (Chao 1984)","title":"alpha Diversity","text":"ŜChao1={S+n−1nF122F2F2>0S+n−1nF1(F1−1)2F2=0 \\hat{S}_{Chao1} = \\begin{cases} S + \\frac{n - 1}{n} \\frac{F_1^2}{2 F_2} & F_2 > 0 \\\\ S + \\frac{n - 1}{n} \\frac{F_1 (F_1 - 1)}{2} & F_2 = 0 \\end{cases} F1F_1 number singleton species F2F_2 number doubleton species. special case homogeneous case, bias-corrected estimator : ŜChao1=S+n−1nF1(F1−1)2F2+1 \\hat{S}_{Chao1} = S + \\frac{n - 1}{n} \\frac{F_1 (F_1 - 1)}{2 F_2 + 1} improved Chao1 estimator (Chiu et al. 2014) makes use additional information tripletons (F3F_3) quadrupletons (F4F_4): ŜiChao1=ŜChao1+n−34nF3F4×max(F1−n−3n−1F2F32F4,0) \\hat{S}_{iChao1} = \\hat{S}_{Chao1} + \\frac{n - 3}{4 n} \\frac{F_3}{F_4} \\times \\max\\left(F_1 - \\frac{n - 3}{n - 1} \\frac{F_2 F_3}{2 F_4} , 0\\right)","code":"index_chao1(woodland) #> [1] 22.65098 index_chao1(woodland, unbiased = TRUE) #> [1] 21.49118 index_chao1(woodland, improved = TRUE)"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"abundance-based-coverage-estimator-chao-lee-1992","dir":"Articles","previous_headings":"Richness > Asymptotic Species Richness","what":"Abundance-based Coverage Estimator (Chao & Lee 1992)","title":"alpha Diversity","text":"ŜACE=Ŝabun+ŜrareĈACE+F1ĈACE×γ̂ACE2 \\hat{S}_{ACE} = \\hat{S}_{abun} + \\frac{\\hat{S}_{rare}}{\\hat{C}_{ACE}} + \\frac{F_1}{\\hat{C}_{ACE}} \\times \\hat{\\gamma}^2_{ACE} Ŝrare=∑=1kFi\\hat{S}_{rare} = \\sum_{= 1}^{k} F_i number rare taxa, Ŝabun=∑>kNFi\\hat{S}_{abun} = \\sum_{> k}^{N} F_i number abundant taxa (given cut-value kk), ĈACE=1−F1nrare\\hat{C}_{ACE} = 1 - \\frac{F_1}{n_{rare}} Turing’s coverage estimate : γ̂ACE2=max[ŜrareĈACE∑=1ki(−1)Finrare(nrare−1)−1,0] \\hat{\\gamma}^2_{ACE} = \\max\\left[\\frac{\\hat{S}_{rare}}{\\hat{C}_{ACE}} \\frac{\\sum_{= 1}^{k} (- 1)F_i}{n_{rare}\\left(n_{rare} - 1\\right)} - 1, 0\\right]","code":"index_ace(woodland) #> [1] 22.0875"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"squares-estimator-alroy-2018","dir":"Articles","previous_headings":"Richness > Asymptotic Species Richness","what":"Squares estimator (Alroy 2018)","title":"alpha Diversity","text":"squares richness estimator designed accurate Chao1 abundance distributions even: Ŝ=S+F12n2−F1×S∑=1Sni2 \\hat{S} = S + \\frac{F_1^2}{n^2 - F_1 \\times S} \\sum_{= 1}^{S} n_i^2","code":"index_squares(woodland) #> [1] 21.92422"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"quadrat-richness","dir":"Articles","previous_headings":"Richness","what":"Quadrat richness","title":"alpha Diversity","text":"replicated incidence data (.e. presence/absence data two sampled quadrats equal size; m×pm \\times p incidence matrix), Chao2 estimator :","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"chao2-estimator-chao-1987","dir":"Articles","previous_headings":"Richness > Quadrat richness","what":"Chao2 estimator (Chao 1987)","title":"alpha Diversity","text":"ŜChao2={S+m−1mq122q2q2>0S+m−1mq1(q1−1)2q2=0 \\hat{S}_{Chao2} = \\begin{cases} S + \\frac{m - 1}{m} \\frac{q_1^2}{2 q_2} & q_2 > 0 \\\\ S + \\frac{m - 1}{m} \\frac{q_1 (q_1 - 1)}{2} & q_2 = 0 \\end{cases} Improved Chao2 estimator (Chiu et al. 2014): ŜiChao2=ŜChao2+m−34mq3q4×max(q1−m−3m−1q2q32q4,0) \\hat{S}_{iChao2} = \\hat{S}_{Chao2} + \\frac{m - 3}{4 m} \\frac{q_3}{q_4} \\times \\max\\left(q_1 - \\frac{m - 3}{m - 1} \\frac{q_2 q_3}{2 q_4} , 0\\right)","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"incidence-based-coverage-estimator-chao-chiu-2016","dir":"Articles","previous_headings":"Richness > Quadrat richness","what":"Incidence-based Coverage Estimator (Chao & Chiu 2016)","title":"alpha Diversity","text":"ŜICE=Ŝfreq+ŜinfreqĈinfreq+q1Ĉinfreq×γ̂infreq2 \\hat{S}_{ICE} = \\hat{S}_{freq} + \\frac{\\hat{S}_{infreq}}{\\hat{C}_{infreq}} + \\frac{q_1}{\\hat{C}_{infreq}} \\times \\hat{\\gamma}^2_{infreq} Ŝinfreq=∑=1kqi\\hat{S}_{infreq} = \\sum_{= 1}^{k} q_i number infrequent taxa, Ŝfreq=∑>kNqi\\hat{S}_{freq} = \\sum_{> k}^{N} q_i number frequent taxa (given cut-value kk), Ĉinfreq=1−Q1∑=1kiqi\\hat{C}_{infreq} = 1 - \\frac{Q_1}{\\sum_{= 1}^{k} iq_i} Turing’s coverage estimate : γ̂infreq2=max[ŜinfreqĈinfreqminfreqminfreq−1∑=1ki(−1)qi(∑=1kiqi)(∑=1kiqi−1)−1,0] \\hat{\\gamma}^2_{infreq} = \\max\\left[\\frac{\\hat{S}_{infreq}}{\\hat{C}_{infreq}} \\frac{m_{infreq}}{m_{infreq} - 1} \\frac{\\sum_{= 1}^{k} (- 1)q_i}{\\left(\\sum_{= 1}^{k} iq_i\\right)\\left(\\sum_{= 1}^{k} iq_i - 1\\right)} - 1, 0\\right] minfreqm_{infreq} number sampling units include least one infrequent species.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"rarefaction","dir":"Articles","previous_headings":"","what":"Rarefaction","title":"alpha Diversity","text":"always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (Ŝ\\hat{S}) number taxa expected samples standard size nn (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":"## Baxter rarefaction RA <- rarefaction(mississippi, sample = 100, method = \"baxter\") plot(RA)"},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"hurlbert-1971-unbiased-estimate-of-sander-1968-rarefaction","dir":"Articles","previous_headings":"Rarefaction","what":"Hurlbert (1971) unbiased estimate of Sander (1968) rarefaction","title":"alpha Diversity","text":"E(S)=∑=1S[1−(n−nim)(nm)] E(S) = \\sum_{= 1}^{S} \\left[ 1 - \\frac{{n - n_i} \\choose m}{n \\choose m} \\right]","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"baxter-2001-rarefaction","dir":"Articles","previous_headings":"Rarefaction","what":"Baxter (2001) rarefaction","title":"alpha Diversity","text":"E(S)=∑=1S[1−(n−ni)!(n−m)!(n−ni−m)!n!] E(S) = \\sum_{= 1}^{S} \\left[ 1 - \\frac{(n - n_i)!(n - m)!}{(n - n_i - m)!n!} \\right] : SS number observed species/types, nin_i number individuals ii-th species/type, n=∑=1Snin = \\sum_{= 1}^{S} n_i total number individuals, mm sub-sample size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/alpha.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"alpha Diversity","text":"Alroy, J. (2018). Limits Species Richness Terrestrial Communities. Ecology Letters, 21(12): 1781-1789. 10.1111/ele.13152. Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4): 715-725. DOI: 10.2307/2694184. Berger, W. H. & Parker, F. L. (1970). Diversity Planktonic Foraminifera Deep Sea Sediments. Science, 168(3937): 1345-1347. DOI: 10.1126/science.168.3937.1345. Bobrowsky, P. T. & Ball, B. F. (1989). Theory Mechanics Ecological Diversity Archaeology. R. D. Leonard & G. T. Jones (Eds.), Quantifying Diversity Archaeology, 4-12. New Directions Archaeology. Cambridge: Cambridge University Press. Bowman, K. O., Hutcheson, K., Odum, E. P. & Shenton, L. R. (1971). Comments Distribution Indices Diversity. E. C. Patil, E. C. Pielou & W. E. Waters (Eds.), Statistical Ecology, 3:315-366. University Park, PA: Pennsylvania State University Press. Brillouin, L. (1956). Science Information Theory. New York: Academic Press. Chao, . (1984). Nonparametric Estimation Number Classes Population. Scandinavian Journal Statistics, 11(4): 265-270. Chao, . (1987). Estimating Population Size Capture Recapture Data Unequal Catchability. Biometrics, 43(4): 783-791. DOI: 10.2307/2531532. Chao, . & Lee, S.-M. (1992). Estimating Number Classes Via Sample Coverage. Journal American Statistical Association, 87(417): 210-217. DOI: 10.1080/01621459.1992.10475194. Chao, . & Chiu, C.-H. (2016). Species Richness: Estimation Comparison. N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri & J. L. Teugels (Eds.), Wiley StatsRef: Statistics Reference Online, 1-26. Chichester, UK: John Wiley & Sons, Ltd. DOI: 10.1002/9781118445112.stat03432.pub2. Chiu, C.-H., Wang, Y.-T., Walther, B. . & Chao, . (2014). Improved Nonparametric Lower Bound Species Richness Via Modified Good-Turing Frequency Formula. Biometrics, 70(3): 671-682. DOI: 10.1111/biom.12200. de Caprariis, P., Lindemann, R. H. & Collins, C. M. (1976). Method Determining Optimum Sample Size Species Diversity Studies. Journal International Association Mathematical Geology, 8(5): 575-581. DOI: 10.1007/BF01042995. Fisher, R. ., Corbet, . S. & Williams, C. B. (1943). Relation Number Species Number Individuals Random Sample Animal Population. Journal Animal Ecology, 12(1): 42. DOI: 10.2307/1411. Gleason, H. . (1922). Relation Species Area. Ecology, 3(2): 158-162. DOI: 10.2307/1929150. Hurlbert, S. H. (1971). Nonconcept Species Diversity: Critique Alternative Parameters. Ecology, 52(4): 577-586. DOI: 10.2307/1934145. Hutcheson, K. (1970). Test Comparing Diversity Based Shannon Formula. Journal Theoretical Biology, 29(1): 151-154. DOI: 10.1016/0022-5193(70)90124-4. Kilburn, P. D. (1966). Analysis Species-Area Relation. Ecology, 47(5): 831-843. DOI: 10.2307/1934269. Laxton, R. R. (1978). Measure Diversity. Journal Theoretical Biology, 70(1): 51-67. DOI: 10.1016/0022-5193(78)90302-8. Macarthur, R. H. (1965). Patterns Species Diversity. Biological Reviews, 40(4): 510-533. DOI: 10.1111/j.1469-185X.1965.tb00815.x. Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. Margalef, R. (1958). Information Theory Ecology. General Systems, 3: 36-71. McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3): 392-404. DOI: 10.2307/1932674. Menhinick, E. F. 1964. Comparison Species-Individuals Diversity Indices Applied Samples Field Insects. Ecology, 45(4): 859-861. 10.2307/1934933. Odum, H. T., Cantlon, J. E. Kornicker, L. S. (1960). Organizational Hierarchy Postulate Interpretation Species-Individual Distributions, Species Entropy, Ecosystem Evolution, Meaning Species-Variety Index. Ecology, 41(2): 395-395. 10.2307/1930248. Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. DOI: 10.1146/annurev.es.05.110174.001441. Pielou, E. C. (1975). Ecological Diversity. New York: Wiley. Preston, F. W. (1948). Commonness, Rarity, Species. Ecology, 29(3): 254-283. DOI: 10.2307/1930989. Preston, F. W. (1962a). Canonical Distribution Commonness Rarity: Part . Ecology, 43(2): 185. DOI: 10.2307/1931976. Preston, F. W. (1962b). Canonical Distribution Commonness Rarity: Part II. Ecology, 43(3): 410-432. DOI: 10.2307/1933371. Sander, H. L. (1968). Marine Benthic Diversity: Comparative Study. American Naturalist, 102(925): 243-282. Shannon, C. E. (1948). Mathematical Theory Communication. Bell System Technical Journal, 27, 379-423. DOI: 10.1002/j.1538-7305.1948.tb01338.x. Simpson, E. H. (1949). Measurement Diversity. Nature, 163(4148): 688-688. DOI: 10.1038/163688a0.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/beta.html","id":"turnover","dir":"Articles","previous_headings":"","what":"Turnover","title":"beta Diversity","text":"following methods can used ascertain degree turnover taxa composition along gradient qualitative (presence/absence) data. assumes order matrix rows (1 mm) follows progression along gradient/transect. denote m×pm \\times p incidence matrix X=[xij]∀∈[1,m],j∈[1,p]X = \\left[ x_{ij} \\right] ~\\forall \\\\left[ 1,m \\right], j \\\\left[ 1,p \\right] p×pp \\times p corresponding co-occurrence matrix Y=[yij]∀,j∈[1,p]Y = \\left[ y_{ij} \\right] ~\\forall ,j \\\\left[ 1,p \\right], row column sums: xi⋅=∑j=1pxijx⋅j=∑=1mxijx⋅⋅=∑j=1p∑=1mxij∀xij∈{0,1}yi⋅=∑j≥ipyijy⋅j=∑≤jpyijy⋅⋅=∑=1p∑j≥ipyij∀yij∈{0,1}\\begin{align} x_{\\cdot} = \\sum_{j = 1}^{p} x_{ij} && x_{\\cdot j} = \\sum_{= 1}^{m} x_{ij} && x_{\\cdot \\cdot} = \\sum_{j = 1}^{p} \\sum_{= 1}^{m} x_{ij} && \\forall x_{ij} \\\\lbrace 0,1 \\rbrace \\\\ y_{\\cdot} = \\sum_{j \\geqslant }^{p} y_{ij} && y_{\\cdot j} = \\sum_{\\leqslant j}^{p} y_{ij} && y_{\\cdot \\cdot} = \\sum_{= 1}^{p} \\sum_{j \\geqslant }^{p} y_{ij} && \\forall y_{ij} \\\\lbrace 0,1 \\rbrace \\end{align} Turnover measures. : α\\alpha mean sample diversity: α=x⋅⋅m\\alpha = \\frac{x_{\\cdot \\cdot}}{m}, g(H)g(H) number taxa gained along transect, l(H)l(H) number taxa lost along transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/articles/beta.html","id":"similarity","dir":"Articles","previous_headings":"","what":"Similarity","title":"beta Diversity","text":"Similarity two samples aa bb two types xx yy can measured follow. indices provide scale similarity 00-11 11 perfect similarity 00 similarity, exception Brainerd-Robinson index scaled 00 200200. Qualitative similarity measures (samples). Quantitative similarity measures (samples). Quantitative similarity measures (types). : SaS_a SbS_b denote total number taxa observed samples aa bb, respectively, NaN_a NbN_b denote total number individuals samples aa bb, respectively, aja_j bjb_j denote number individuals jj-th type/taxon, j∈[1,S]j \\\\left[ 1,S \\right], xix_i yiy_i denote number individuals ii-th sample/case, ∈[1,m]\\\\left[ 1,m \\right], oio_i denotes number sample/case common type/taxon: oi=∑k=1mxk∩yko_i = \\sum_{k = 1}^{m} x_k \\cap y_k, ojo_j denotes number type/taxon common sample/case: oj=∑k=1Sak∩bko_j = \\sum_{k = 1}^{S} a_k \\cap b_k.","code":"## Brainerd-Robinson (similarity between assemblages) BR <- similarity(mississippi, method = \"brainerd\") plot_spot(BR, col = khroma::colour(\"YlOrBr\")(12)) ## Binomial co-occurrence (similarity between types) BI <- similarity(mississippi, method = \"binomial\") plot_spot(BI, col = khroma::colour(\"PRGn\")(12))"},{"path":"https://packages.tesselle.org/tabula/dev/articles/beta.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"beta Diversity","text":"Brainerd, G. W. 1951. Place Chronological Ordering Archaeological Analysis. American Antiquity, 16(4), 301-313. DOI: 10.2307/276979. Bray, J. R. & Curtis, J. T. (1957). Ordination Upland Forest Communities Southern Wisconsin. Ecological Monographs, 27(4), 325-349. DOI: 10.2307/1942268. Cody, M. L. (1975). Towards Theory Continental Species Diversity: Bird Distributions Mediterranean Habitat Gradients. M. L. Cody & J. M. Diamond (Eds.), Ecology Evolution Communities, 214-257. Cambridge, MA: Harvard University Press. Kintigh, K. (2006). Ceramic Dating Type Associations. J. Hantman & R. (Eds.), Managing Archaeological Data: Essays Honor Sylvia W. Gaines, 17–26. Anthropological Research Paper 57. Tempe, AZ: Arizona State University. DOI: 10.6067/XCV8J38QSS. Moreno, C. E. & Rodríguez, P. (2010). Consistent Terminology Quantifying Species Diversity? Oecologia, 163(2), 279-782. DOI: 10.1007/s00442-010-1591-7. Robinson, W. S. (1951). Method Chronologically Ordering Archaeological Deposits. American Antiquity, 16(4), 293-301. DOI: 10.2307/276978. Routledge, R. D. (1977). Whittaker’s Components Diversity. Ecology, 58(5), 1120-1127. DOI: 10.2307/1936932. Whittaker, R. H. (1960). Vegetation Siskiyou Mountains, Oregon California. Ecological Monographs, 30(3), 279-338. DOI: 10.2307/1943563.. Wilson, M. V. & Shmida, . (1984). Measuring Beta Diversity Presence-Absence Data. Journal Ecology, 72(3), 1055-1064. DOI: 10.2307/2259551.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Analysis and Visualization of Archaeological Count Data","text":"easy way examine archaeological count data. package provides several tests measures diversity: heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). allows easily visualize count data statistical thresholds: rank vs. abundance plots, heatmaps, Ford (1962) Bertin (1977) diagrams, etc. tabula provides methods : Diversity measurement: heterogeneity(), evenness(), richness(), rarefaction(), turnover(). Similarity measurement co-occurrence: similarity(), occurrence(). Assessing sample size significance: bootstrap(), jackknife(), simulate(). Bertin (1977) Ford (1962) (battleship curve) diagrams: plot_bertin(), plot_ford(). Seriograph (Desachy 2004): seriograph(), matrigraph(). Heatmaps: plot_heatmap(), plot_spot(). kairos companion package tabula provides functions chronological modeling dating archaeological assemblages count data. cite tabula publications use: Frerebeau N (2019). “tabula: R Package Analysis, Seriation, Visualization Archaeological Count Data.” Journal Open Source Software, 4(44). doi:10.21105/joss.01821 https://doi.org/10.21105/joss.01821. Frerebeau N (2024). tabula: Analysis Visualization Archaeological Count Data. Université Bordeaux Montaigne, Pessac, France. doi:10.5281/zenodo.1489944 https://doi.org/10.5281/zenodo.1489944, R package version 3.1.0, https://packages.tesselle.org/tabula/. package part tesselle project https://www.tesselle.org.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analysis and Visualization of Archaeological Count Data","text":"can install released version tabula CRAN : development version GitHub :","code":"install.packages(\"tabula\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/tabula\")"},{"path":"https://packages.tesselle.org/tabula/dev/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Analysis and Visualization of Archaeological Count Data","text":"assumes keep data tidy: variable (type/taxa) must saved column observation (sample/case) must saved row.","code":"## Install extra packages (if needed) # install.packages(\"folio\") ## Load packages library(tabula) ## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\") ## Ford diagram plot_ford(mississippi) ## Co-occurrence of ceramic types mississippi |> occurrence() |> plot_spot() ## Data from Conkey 1980, Kintigh 1989, p. 28 data(\"chevelon\", package = \"folio\") ## Measure diversity by comparing to simulated assemblages set.seed(12345) chevelon |> heterogeneity(method = \"shannon\") |> simulate() |> plot() chevelon |> richness(method = \"count\") |> simulate() |> plot()"},{"path":"https://packages.tesselle.org/tabula/dev/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Analysis and Visualization of Archaeological Count Data","text":"Please note tabula project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/paper.html","id":null,"dir":"","previous_headings":"","what":"Background","title":"Background","text":"Detecting quantifying material cultural variations time space important methodological issues archaeology. solve issues, need construct reliable chronologies quantitative descriptions archaeological assemblages, . e. archaeological sites intrasite units, described set pp different objects. Building chronologies involves distinguishing relative (providing chronological sequence) absolute dating methods (yield calendric indicators) [@obrien2002]. Within relative dating, matrix seriation long-established method—first formulated @petrie1899—allowed construction reference chronologies [@ihm2005]. set XX nn archaeological assemblages, seriation problem comes discovering XX order inferred chronological. approach relies set well-defined statistical archaeological assumptions [@dunnell1970]. may use priori information, e.g., absolute dates stratigraphical constraints [@poblome2003] allows analysis chronological patterns socio-economic cultural perspective (e.g., [@bellanger2012], [@lipo2015]). quantitative analysis archaeological assemblages can thus carried synchronic, e.g., diversity measurements, diachronic, e.g., evolutionary studies: selection process, patterns cultural transmission, etc., way. approaches cover wide range applications led development multitude statistical models, none systematically implemented enable deployment reproducible workflows.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/paper.html","id":"summary","dir":"","previous_headings":"","what":"Summary","title":"Background","text":"tabula provides convenient reproducible toolkit analyzing, seriating, visualizing archaeological count data, artifacts faunal remains. Several R packages, e.g. ade4 [@dray2007], SpadeR [@chao2016] vegan [@oksanen2019], allow estimation diversity indices implement seriation/ordination methods, packages mainly oriented towards ecological issues. tabula provides archaeologically-orientated implementations allow integration specific data (dates, stratigraphy, etc.) offers consistent framework. latter particular value since tabula designed used archaeologists students little background courses dating methods applied statistics archaeology. package uses set S4 classes archaeological data matrices extend matrix data type. new classes represent different specialized matrices: incidence, abundance, co-occurrence, (dis)similarity. Methods variety functions applied objects classes provide tools relative absolute dating analysis (chronological) patterns. tabula includes functions matrix seriation (seriate_*), well chronological modeling dating (date_*) archaeological assemblages objects. Resulting models can checked stability refined resampling methods (refine_*). Estimated dates can displayed tempo activity plot [@dye2016] assess rhythms long periods. Beyond , tabula provides several tests (test_*) measures diversity within archaeological assemblages (index_*): heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). Finally, package makes easy visualize count data statistical thresholds (plot_*): rank vs. abundance plots, heatmaps, @ford1962 @bertin1977 diagrams.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/paper.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Background","text":"following contributors made possible develop project helpful discussion bringing new ideas: Jean-Baptiste Fourvel, Brice Lebrun, Ben Marwick, Matthew Peeples, Anne Philippe.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/CompositionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Composition Index — CompositionIndex-class","title":"Composition Index — CompositionIndex-class","text":"S4 class represent composition measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/CompositionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Composition Index — CompositionIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/CompositionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Composition Index — CompositionIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Index — DiversityIndex-class","title":"Diversity Index — DiversityIndex-class","text":"S4 class represent diversity measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Diversity Index — DiversityIndex-class","text":".Data numeric vector diversity measures. labels character vector giving sample names. size integer vector giving sample sizes. data numeric matrix count data. method character string specifying method used. simulation four columns numeric matrix giving diversity measures simulated assemblage (sample size, mean estimate, lower upper boundaries confidence interval).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Diversity Index — DiversityIndex-class","text":"class inherits base numeric.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Diversity Index — DiversityIndex-class","text":"code snippets , x DiversityIndex object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/DiversityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Index — DiversityIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/EvennessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenness Index — EvennessIndex-class","title":"Evenness Index — EvennessIndex-class","text":"S4 class represent evenness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/EvennessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Evenness Index — EvennessIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/EvennessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evenness Index — EvennessIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/HeterogeneityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity Index — HeterogeneityIndex-class","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"S4 class represent heterogeneity measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/HeterogeneityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/HeterogeneityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RarefactionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Index — RarefactionIndex-class","title":"Rarefaction Index — RarefactionIndex-class","text":"S4 class represent rarefaction measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RarefactionIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rarefaction Index — RarefactionIndex-class","text":".Data numeric matrix count data (expected taxa per fixed number individuals). labels character vector giving sample names. size integer vector giving sample sizes. method character string specifying method used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RarefactionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rarefaction Index — RarefactionIndex-class","text":"class inherits base matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/RarefactionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Index — RarefactionIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RichnessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness Index — RichnessIndex-class","title":"Richness Index — RichnessIndex-class","text":"S4 class represent richness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/RichnessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Richness Index — RichnessIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/RichnessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness Index — RichnessIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/aves.html","id":null,"dir":"Reference","previous_headings":"","what":"Birds Species and Abundances — aves","title":"Birds Species and Abundances — aves","text":"dataset birds species abundances managed unmanaged areas along River Wye (UK).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/aves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Birds Species and Abundances — aves","text":"","code":"aves"},{"path":"https://packages.tesselle.org/tabula/dev/reference/aves.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birds Species and Abundances — aves","text":"data.frame 2 rows 26 variables (bird species).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/aves.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birds Species and Abundances — aves","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Estimation — bootstrap","title":"Bootstrap Estimation — bootstrap","text":"Samples randomly elements object replacement.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Estimation — bootstrap","text":"","code":"# S4 method for class 'DiversityIndex' bootstrap(object, n = 1000, f = NULL)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Estimation — bootstrap","text":"object R object (typically DiversityIndex object). n non-negative integer giving number bootstrap replications. f function takes single numeric vector (result ) argument.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Estimation — bootstrap","text":"f NULL (default), bootstrap() returns named numeric vector following elements: original observed value applied object. mean bootstrap estimate mean . bias bootstrap estimate bias . error bootstrap estimate standard error . f function, bootstrap() returns result f applied n values .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Estimation — bootstrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Estimation — bootstrap","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187 ## Bootstrap resampling bootstrap(h, f = NULL) #> original mean bias error #> Altamira 3.269200 3.278498 0.009297979 0.08048385 #> Cueto de la Mina 2.955298 2.972580 0.017281827 0.15700290 #> El Juyo 2.491683 2.521575 0.029891792 0.19021698 #> El Cierro 2.485604 2.487144 0.001539771 0.22464720 #> La Paloma 2.329187 2.310936 -0.018250257 0.26487280 bootstrap(h, f = summary) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> Altamira 2.992719 3.222878 3.280765 3.276743 3.334473 3.549729 #> Cueto de la Mina 2.356353 2.894522 2.983316 2.977874 3.078325 3.369425 #> El Juyo 1.721241 2.376338 2.521911 2.515313 2.655347 3.117557 #> El Cierro 1.559581 2.351641 2.510941 2.486987 2.634027 2.978305 #> La Paloma 1.273028 2.151884 2.334882 2.308805 2.491505 2.931322 quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #> 25% 50% #> Altamira 3.229277 3.287153 #> Cueto de la Mina 2.888914 2.986238 #> El Juyo 2.406740 2.535562 #> El Cierro 2.341829 2.499437 #> La Paloma 2.152664 2.339654"},{"path":"https://packages.tesselle.org/tabula/dev/reference/cantabria.html","id":null,"dir":"Reference","previous_headings":"","what":"Early Magdalenian Engraved Bones — cantabria","title":"Early Magdalenian Engraved Bones — cantabria","text":"dataset design elements engraved bones Cantabrian Spain.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/cantabria.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Early Magdalenian Engraved Bones — cantabria","text":"","code":"cantabria"},{"path":"https://packages.tesselle.org/tabula/dev/reference/cantabria.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Early Magdalenian Engraved Bones — cantabria","text":"data.frame 5 rows 44 variables (designs).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/cantabria.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Early Magdalenian Engraved Bones — cantabria","text":"Conkey, M. W. (1980). Identification prehistoric hunter-gatherer aggregation sites: case Altamira. Current Anthropology, 21(5), 609-630. Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness — heterogeneity","title":"Heterogeneity and Evenness — heterogeneity","text":"heterogeneity() computes heterogeneity dominance index. evenness() computes evenness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heterogeneity and Evenness — heterogeneity","text":"","code":"heterogeneity(object, ...) evenness(object, ...) # S4 method for class 'matrix' heterogeneity( object, ..., method = c(\"berger\", \"boone\", \"brillouin\", \"mcintosh\", \"shannon\", \"simpson\") ) # S4 method for class 'data.frame' heterogeneity( object, ..., method = c(\"berger\", \"boone\", \"brillouin\", \"mcintosh\", \"shannon\", \"simpson\") ) # S4 method for class 'matrix' evenness( object, ..., method = c(\"shannon\", \"brillouin\", \"mcintosh\", \"simpson\") ) # S4 method for class 'data.frame' evenness( object, ..., method = c(\"shannon\", \"brillouin\", \"mcintosh\", \"simpson\") )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heterogeneity and Evenness — heterogeneity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods (see ). method character string specifying index computed (see details). unambiguous substring can given. evenness logical scalar: evenness measure computed instead heterogeneity/dominance index?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heterogeneity and Evenness — heterogeneity","text":"heterogeneity() returns HeterogeneityIndex object. evenness() returns EvennessIndex object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Heterogeneity and Evenness — heterogeneity","text":"Diversity measurement assumes individuals specific taxa equivalent types equally different (Peet 1974). measure diversity can achieved using indices built relative abundance taxa. indices (sometimes referred non-parametric indices) benefit making assumptions underlying distribution taxa abundance: take relative abundances species present species richness account. Peet (1974) refers indices heterogeneity. Diversity indices focus one aspect taxa abundance emphasize either richness (weighting towards uncommon taxa) dominance (weighting towards abundant taxa; Magurran 1988). Evenness measure evenly individuals distributed across sample.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"heterogeneity-and-evenness-measures","dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness Measures","title":"Heterogeneity and Evenness — heterogeneity","text":"following heterogeneity index corresponding evenness measures available (see Magurran 1988 details): berger Berger-Parker dominance index. boone Boone heterogeneity measure. brillouin Brillouin diversity index. mcintosh McIntosh dominance index. shannon Shannon-Wiener diversity index. simpson Simpson dominance index. berger, mcintosh simpson methods return dominance index, reciprocal inverse form usually adopted, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Heterogeneity and Evenness — heterogeneity","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity and Evenness — heterogeneity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/heterogeneity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heterogeneity and Evenness — heterogeneity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187 (e <- evenness(cantabria, method = \"shannon\")) #> [1] 0.8987278 0.8966760 0.8462335 0.9178574 0.9373336 plot(h)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":null,"dir":"Reference","previous_headings":"","what":"Abundance-based Coverage Estimator — index_ace","title":"Abundance-based Coverage Estimator — index_ace","text":"Abundance-based Coverage Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abundance-based Coverage Estimator — index_ace","text":"","code":"index_ace(x, ...) # S4 method for class 'numeric' index_ace(x, k = 10, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abundance-based Coverage Estimator — index_ace","text":"x numeric vector count data (absolute frequencies). ... Currently used. k length-one numeric vector giving threshold rare/infrequent abundant/frequent species. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Abundance-based Coverage Estimator — index_ace","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Abundance-based Coverage Estimator — index_ace","text":"Chao, . & Lee, S.-M. (1992). Estimating Number Classes via Sample Coverage. Journal American Statistical Association, 87(417), 210-217. doi:10.1080/01621459.1992.10475194 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ace.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Abundance-based Coverage Estimator — index_ace","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":null,"dir":"Reference","previous_headings":"","what":"Baxter's Rarefaction — index_baxter","title":"Baxter's Rarefaction — index_baxter","text":"Baxter's Rarefaction","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Baxter's Rarefaction — index_baxter","text":"","code":"index_baxter(x, ...) # S4 method for class 'numeric' index_baxter(x, sample, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Baxter's Rarefaction — index_baxter","text":"x numeric vector count data (absolute frequencies). ... Currently used. sample length-one numeric vector giving sub-sample size. size sample smaller total community size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Baxter's Rarefaction — index_baxter","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Baxter's Rarefaction — index_baxter","text":"Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_baxter.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Baxter's Rarefaction — index_baxter","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":null,"dir":"Reference","previous_headings":"","what":"Berger-Parker Dominance Index — index_berger","title":"Berger-Parker Dominance Index — index_berger","text":"Berger-Parker Dominance Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Berger-Parker Dominance Index — index_berger","text":"","code":"index_berger(x, ...) # S4 method for class 'numeric' index_berger(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Berger-Parker Dominance Index — index_berger","text":"x numeric vector count data (absolute frequencies). ... Currently used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Berger-Parker Dominance Index — index_berger","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Berger-Parker Dominance Index — index_berger","text":"Berger-Parker index expresses proportional importance abundant type. metric highly biased sample size richness, moreover make use information available sample. dominance index, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Berger-Parker Dominance Index — index_berger","text":"Berger, W. H. & Parker, F. L. (1970). Diversity Planktonic Foraminifera Deep-Sea Sediments. Science, 168(3937), 1345-1347. doi:10.1126/science.168.3937.1345 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_berger.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Berger-Parker Dominance Index — index_berger","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Binomial Co-Occurrence Assessment — index_binomial","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"Binomial Co-Occurrence Assessment","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"","code":"index_binomial(x, y, ...) # S4 method for class 'numeric,numeric' index_binomial(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"assesses degree co-occurrence taxa/types within dataset. strongest associations shown large positive numbers, strongest segregations large negative numbers.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"Kintigh, K. (2006). Ceramic Dating Type Associations. J. Hantman R. (eds.), Managing Archaeological Data: Essays Honor Sylvia W. Gaines. Anthropological Research Paper, 57. Tempe, AZ: Arizona State University, p. 17-26.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Binomial Co-Occurrence Assessment — index_binomial","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":null,"dir":"Reference","previous_headings":"","what":"Boone Heterogeneity Measure — index_boone","title":"Boone Heterogeneity Measure — index_boone","text":"Boone Heterogeneity Measure","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boone Heterogeneity Measure — index_boone","text":"","code":"index_boone(x, ...) # S4 method for class 'matrix' index_boone(x, j = NULL, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boone Heterogeneity Measure — index_boone","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used. j integer giving index reference type/taxa. NULL (default), frequent type/taxa assemblage used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boone Heterogeneity Measure — index_boone","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Boone Heterogeneity Measure — index_boone","text":"Boone, J. L. (1987). Defining Measuring Midden Catchment. American Antiquity, 52(2), 336-45. doi:10.2307/281785 . Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_boone.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Boone Heterogeneity Measure — index_boone","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":null,"dir":"Reference","previous_headings":"","what":"Brainerd-Robinson Quantitative Index — index_brainerd","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"Brainerd-Robinson Quantitative Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"","code":"index_brainerd(x, y, ...) # S4 method for class 'numeric,numeric' index_brainerd(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"city-block metric similarity pairs samples/cases.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"Brainerd, G. W. (1951). Place Chronological Ordering Archaeological Analysis. American Antiquity, 16(04), 301-313. doi:10.2307/276979 . Robinson, W. S. (1951). Method Chronologically Ordering Archaeological Deposits. American Antiquity, 16(04), 293-301. doi:10.2307/276978 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brainerd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Brainerd-Robinson Quantitative Index — index_brainerd","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorenson Quantitative Index — index_bray","title":"Sorenson Quantitative Index — index_bray","text":"Bray Curtis modified version Sorenson index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorenson Quantitative Index — index_bray","text":"","code":"index_bray(x, y, ...) # S4 method for class 'numeric,numeric' index_bray(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorenson Quantitative Index — index_bray","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorenson Quantitative Index — index_bray","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sorenson Quantitative Index — index_bray","text":"Bray, J. R. & Curtis, J. T. (1957). Ordination Upland Forest Communities Southern Wisconsin. Ecological Monographs, 27(4), 325-349. doi:10.2307/1942268 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_bray.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sorenson Quantitative Index — index_bray","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":null,"dir":"Reference","previous_headings":"","what":"Brillouin Diversity Index. — index_brillouin","title":"Brillouin Diversity Index. — index_brillouin","text":"Brillouin Diversity Index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Brillouin Diversity Index. — index_brillouin","text":"","code":"index_brillouin(x, ...) # S4 method for class 'numeric' index_brillouin(x, evenness = FALSE, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Brillouin Diversity Index. — index_brillouin","text":"x numeric vector count data (absolute frequencies). ... Currently used. evenness numeric scalar: evenness computed? na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Brillouin Diversity Index. — index_brillouin","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Brillouin Diversity Index. — index_brillouin","text":"Brillouin index describes known collection: assume random sampling infinite population. Pielou (1975) Laxton (1978) argues use Brillouin index circumstances, especially preference Shannon index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Brillouin Diversity Index. — index_brillouin","text":"Ramanujan approximation used \\(x!\\) computation \\(x > 170\\).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Brillouin Diversity Index. — index_brillouin","text":"Brillouin, L. (1956). Science information theory. New York: Academic Press. Laxton, R. R. (1978). measure diversity. Journal Theoretical Biology, 70(1), 51-67. doi:10.1016/0022-5193(78)90302-8 . Pielou, E. C. (1975). Ecological Diversity. New York: Wiley. doi:10.4319/lo.1977.22.1.0174b","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_brillouin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Brillouin Diversity Index. — index_brillouin","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":null,"dir":"Reference","previous_headings":"","what":"Chao1 Estimator — index_chao1","title":"Chao1 Estimator — index_chao1","text":"Chao1 Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chao1 Estimator — index_chao1","text":"","code":"index_chao1(x, ...) # S4 method for class 'numeric' index_chao1(x, unbiased = FALSE, improved = FALSE, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chao1 Estimator — index_chao1","text":"x numeric vector count data (absolute frequencies). ... Currently used. unbiased logical scalar: bias-corrected estimator used? improved logical scalar: improved estimator used? na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Chao1 Estimator — index_chao1","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chao1 Estimator — index_chao1","text":"Chao, . (1984). Nonparametric Estimation Number Classes Population. Scandinavian Journal Statistics, 11(4), 265-270. Chiu, C.-H., Wang, Y.-T., Walther, B. . & Chao, . (2014). improved nonparametric lower bound species richness via modified good-turing frequency formula. Biometrics, 70(3), 671-682. doi:10.1111/biom.12200 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao1.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Chao1 Estimator — index_chao1","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":null,"dir":"Reference","previous_headings":"","what":"Chao2 Estimator — index_chao2","title":"Chao2 Estimator — index_chao2","text":"Chao2 Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chao2 Estimator — index_chao2","text":"","code":"index_chao2(x, ...) # S4 method for class 'matrix' index_chao2(x, unbiased = FALSE, improved = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chao2 Estimator — index_chao2","text":"x \\(m \\times p\\) matrix presence/absence data (incidence). ... Currently used. unbiased logical scalar: bias-corrected estimator used? improved logical scalar: improved estimator used?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Chao2 Estimator — index_chao2","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chao2 Estimator — index_chao2","text":"Chao, . (1987). Estimating Population Size Capture-Recapture Data Unequal Catchability. Biometrics 43(4), 783-791. Chiu, C.-H., Wang, Y.-T., Walther, B. . & Chao, . (2014). improved nonparametric lower bound species richness via modified good-turing frequency formula. Biometrics, 70(3), 671-682. doi:10.2307/2531532 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_chao2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Chao2 Estimator — index_chao2","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":null,"dir":"Reference","previous_headings":"","what":"Cody Measure — index_cody","title":"Cody Measure — index_cody","text":"Cody Measure","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cody Measure — index_cody","text":"","code":"index_cody(x, ...) # S4 method for class 'matrix' index_cody(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cody Measure — index_cody","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cody Measure — index_cody","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cody Measure — index_cody","text":"assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cody Measure — index_cody","text":"Cody, M. L. (1975). Towards theory continental species diversity: Bird distributions Mediterranean habitat gradients. M. L. Cody & J. M. Diamond (Eds.), Ecology Evolution Communities. Cambridge, MA: Harvard University Press, p. 214-257.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_cody.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cody Measure — index_cody","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":null,"dir":"Reference","previous_headings":"","what":"Hurlbert's Rarefaction — index_hurlbert","title":"Hurlbert's Rarefaction — index_hurlbert","text":"Hurlbert's unbiased estimate Sander's rarefaction.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hurlbert's Rarefaction — index_hurlbert","text":"","code":"index_hurlbert(x, ...) # S4 method for class 'numeric' index_hurlbert(x, sample, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hurlbert's Rarefaction — index_hurlbert","text":"x numeric vector count data (absolute frequencies). ... Currently used. sample length-one numeric vector giving sub-sample size. size sample smaller total community size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hurlbert's Rarefaction — index_hurlbert","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hurlbert's Rarefaction — index_hurlbert","text":"Hurlbert, S. H. (1971). Nonconcept Species Diversity: Critique Alternative Parameters. Ecology, 52(4), 577-586. doi:10.2307/1934145 . Sander, H. L. (1968). Marine Benthic Diversity: Comparative Study. American Naturalist, 102(925), 243-282.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_hurlbert.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Hurlbert's Rarefaction — index_hurlbert","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence-based Coverage Estimator — index_ice","title":"Incidence-based Coverage Estimator — index_ice","text":"Incidence-based Coverage Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence-based Coverage Estimator — index_ice","text":"","code":"index_ice(x, ...) # S4 method for class 'matrix' index_ice(x, k = 10, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence-based Coverage Estimator — index_ice","text":"x \\(m \\times p\\) matrix presence/absence data (incidence). ... Currently used. k length-one numeric vector giving threshold rare/infrequent abundant/frequent species.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence-based Coverage Estimator — index_ice","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incidence-based Coverage Estimator — index_ice","text":"Chao, . & Chiu, C.-H. (2016). Species Richness: Estimation Comparison. Balakrishnan, N., Colton, T., Everitt, B., Piegorsch, B., Ruggeri, F. & Teugels, J. L. (Eds.), Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd., 1-26. doi:10.1002/9781118445112.stat03432.pub2","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_ice.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Incidence-based Coverage Estimator — index_ice","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":null,"dir":"Reference","previous_headings":"","what":"Jaccard Index — index_jaccard","title":"Jaccard Index — index_jaccard","text":"Jaccard Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jaccard Index — index_jaccard","text":"","code":"index_jaccard(x, y, ...) # S4 method for class 'character,character' index_jaccard(x, y) # S4 method for class 'logical,logical' index_jaccard(x, y) # S4 method for class 'numeric,numeric' index_jaccard(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jaccard Index — index_jaccard","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jaccard Index — index_jaccard","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Jaccard Index — index_jaccard","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_jaccard.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jaccard Index — index_jaccard","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":null,"dir":"Reference","previous_headings":"","what":"Margalef Richness Index — index_margalef","title":"Margalef Richness Index — index_margalef","text":"Margalef Richness Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Margalef Richness Index — index_margalef","text":"","code":"index_margalef(x, ...) # S4 method for class 'numeric' index_margalef(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Margalef Richness Index — index_margalef","text":"x numeric vector count data (absolute frequencies). ... Currently used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Margalef Richness Index — index_margalef","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Margalef Richness Index — index_margalef","text":"Margalef, R. (1958). Information Theory Ecology. General Systems, 3, 36-71.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_margalef.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Margalef Richness Index — index_margalef","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":null,"dir":"Reference","previous_headings":"","what":"McIntosh Dominance Index. — index_mcintosh","title":"McIntosh Dominance Index. — index_mcintosh","text":"McIntosh Dominance Index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McIntosh Dominance Index. — index_mcintosh","text":"","code":"index_mcintosh(x, ...) # S4 method for class 'numeric' index_mcintosh(x, evenness = FALSE, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"McIntosh Dominance Index. — index_mcintosh","text":"x numeric vector count data (absolute frequencies). ... Currently used. evenness numeric scalar: evenness computed? na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"McIntosh Dominance Index. — index_mcintosh","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"McIntosh Dominance Index. — index_mcintosh","text":"McIntosh index expresses heterogeneity sample geometric terms. describes sample point \\(S\\)-dimensional hypervolume uses Euclidean distance point origin. dominance index, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"McIntosh Dominance Index. — index_mcintosh","text":"McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_mcintosh.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"McIntosh Dominance Index. — index_mcintosh","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":null,"dir":"Reference","previous_headings":"","what":"Menhinick Richness Index — index_menhinick","title":"Menhinick Richness Index — index_menhinick","text":"Menhinick Richness Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Menhinick Richness Index — index_menhinick","text":"","code":"index_menhinick(x, ...) # S4 method for class 'numeric' index_menhinick(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Menhinick Richness Index — index_menhinick","text":"x numeric vector count data (absolute frequencies). ... Currently used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Menhinick Richness Index — index_menhinick","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Menhinick Richness Index — index_menhinick","text":"Menhinick, E. F. (1964). Comparison Species-Individuals Diversity Indices Applied Samples Field Insects. Ecology, 45(4), 859-861. doi:10.2307/1934933 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_menhinick.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Menhinick Richness Index — index_menhinick","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":null,"dir":"Reference","previous_headings":"","what":"Morisita-Horn Quantitative Index — index_morisita","title":"Morisita-Horn Quantitative Index — index_morisita","text":"Morisita-Horn Quantitative Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Morisita-Horn Quantitative Index — index_morisita","text":"","code":"index_morisita(x, y, ...) # S4 method for class 'numeric,numeric' index_morisita(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Morisita-Horn Quantitative Index — index_morisita","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Morisita-Horn Quantitative Index — index_morisita","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Morisita-Horn Quantitative Index — index_morisita","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_morisita.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Morisita-Horn Quantitative Index — index_morisita","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":null,"dir":"Reference","previous_headings":"","what":"Routledge Measures — index_routledge","title":"Routledge Measures — index_routledge","text":"Routledge Measures","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Routledge Measures — index_routledge","text":"","code":"index_routledge1(x, ...) index_routledge2(x, ...) index_routledge3(x, ...) # S4 method for class 'matrix' index_routledge1(x) # S4 method for class 'matrix' index_routledge2(x) # S4 method for class 'matrix' index_routledge3(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Routledge Measures — index_routledge","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Routledge Measures — index_routledge","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Routledge Measures — index_routledge","text":"assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Routledge Measures — index_routledge","text":"Routledge, R. D. (1977). Whittaker's Components Diversity. Ecology, 58(5), 1120-1127. doi:10.2307/1936932 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_routledge.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Routledge Measures — index_routledge","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":null,"dir":"Reference","previous_headings":"","what":"Shannon-Wiener Diversity Index — index_shannon","title":"Shannon-Wiener Diversity Index — index_shannon","text":"Shannon-Wiener Diversity Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shannon-Wiener Diversity Index — index_shannon","text":"","code":"index_shannon(x, ...) # S4 method for class 'numeric' index_shannon( x, evenness = FALSE, unbiased = FALSE, ACE = FALSE, base = exp(1), na.rm = FALSE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shannon-Wiener Diversity Index — index_shannon","text":"x numeric vector count data (absolute frequencies). ... Currently used. evenness numeric scalar: evenness computed? unbiased logical scalar: bias-corrected estimator used? ACE logical scalar: ACE species richness estimator used bias correction? base positive numeric value specifying base respect logarithms computed. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shannon-Wiener Diversity Index — index_shannon","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Shannon-Wiener Diversity Index — index_shannon","text":"Shannon index assumes individuals randomly sampled infinite population taxa represented sample (reflect sample size). main source error arises failure include taxa sample: error increases proportion species discovered sample declines (Peet 1974, Magurran 1988). maximum likelihood estimator (MLE) used relative abundance, known negatively biased sample size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Shannon-Wiener Diversity Index — index_shannon","text":"Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441 . Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Shannon, C. E. (1948). Mathematical Theory Communication. Bell System Technical Journal, 27, 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_shannon.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Shannon-Wiener Diversity Index — index_shannon","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":null,"dir":"Reference","previous_headings":"","what":"Simpson Dominance Index — index_simpson","title":"Simpson Dominance Index — index_simpson","text":"Simpson Dominance Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simpson Dominance Index — index_simpson","text":"","code":"index_simpson(x, ...) # S4 method for class 'numeric' index_simpson(x, evenness = FALSE, unbiased = FALSE, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simpson Dominance Index — index_simpson","text":"x numeric vector count data (absolute frequencies). ... Currently used. evenness numeric scalar: evenness computed? unbiased logical scalar: bias-corrected estimator used? na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simpson Dominance Index — index_simpson","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simpson Dominance Index — index_simpson","text":"Simpson index expresses probability two individuals randomly picked finite sample belong two different types. can interpreted weighted mean proportional abundances. metric true probability value, ranges \\(0\\) (taxa equally present) \\(1\\) (one taxon dominates community completely). dominance index, increase value index accompanies decrease diversity.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simpson Dominance Index — index_simpson","text":"Simpson, E. H. (1949). Measurement Diversity. Nature, 163(4148), 688-688. doi:10.1038/163688a0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_simpson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simpson Dominance Index — index_simpson","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorenson Qualitative Index — index_sorenson","title":"Sorenson Qualitative Index — index_sorenson","text":"Sorenson Qualitative Index","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorenson Qualitative Index — index_sorenson","text":"","code":"index_sorenson(x, y, ...) # S4 method for class 'logical,logical' index_sorenson(x, y) # S4 method for class 'numeric,numeric' index_sorenson(x, y)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorenson Qualitative Index — index_sorenson","text":"x, y numeric vector. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorenson Qualitative Index — index_sorenson","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sorenson Qualitative Index — index_sorenson","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_sorenson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sorenson Qualitative Index — index_sorenson","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":null,"dir":"Reference","previous_headings":"","what":"Squares Estimator — index_squares","title":"Squares Estimator — index_squares","text":"Squares Estimator","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Squares Estimator — index_squares","text":"","code":"index_squares(x, ...) # S4 method for class 'numeric' index_squares(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Squares Estimator — index_squares","text":"x numeric vector count data (absolute frequencies). ... Currently used. na.rm numeric scalar: missing values (including NaN) removed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Squares Estimator — index_squares","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Squares Estimator — index_squares","text":"Alroy, J. (2018). Limits Species Richness Terrestrial Communities. Ecology Letters, 21(12), 1781-1789. doi:10.1111/ele.13152 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_squares.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Squares Estimator — index_squares","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":null,"dir":"Reference","previous_headings":"","what":"Whittaker Measure — index_whittaker","title":"Whittaker Measure — index_whittaker","text":"Whittaker Measure","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Whittaker Measure — index_whittaker","text":"","code":"index_whittaker(x, ...) # S4 method for class 'matrix' index_whittaker(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Whittaker Measure — index_whittaker","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Whittaker Measure — index_whittaker","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Whittaker Measure — index_whittaker","text":"assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Whittaker Measure — index_whittaker","text":"Whittaker, R. H. (1960). Vegetation Siskiyou Mountains, Oregon California. Ecological Monographs, 30(3), 279-338. doi:10.2307/1943563 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_whittaker.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Whittaker Measure — index_whittaker","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":null,"dir":"Reference","previous_headings":"","what":"Wilson Measure — index_wilson","title":"Wilson Measure — index_wilson","text":"Wilson Measure","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wilson Measure — index_wilson","text":"","code":"index_wilson(x, ...) # S4 method for class 'matrix' index_wilson(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wilson Measure — index_wilson","text":"x \\(m \\times p\\) numeric matrix count data (absolute frequencies, .e. contingency table). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wilson Measure — index_wilson","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wilson Measure — index_wilson","text":"assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wilson Measure — index_wilson","text":"Wilson, M. V., & Shmida, . (1984). Measuring Beta Diversity Presence-Absence Data. Journal Ecology, 72(3), 1055-1064. doi:10.2307/2259551 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/index_wilson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Wilson Measure — index_wilson","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":null,"dir":"Reference","previous_headings":"","what":"Jackknife Estimation — jackknife","title":"Jackknife Estimation — jackknife","text":"Jackknife Estimation","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jackknife Estimation — jackknife","text":"","code":"# S4 method for class 'DiversityIndex' jackknife(object, f = NULL)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jackknife Estimation — jackknife","text":"object R object (typically DiversityIndex object). f function takes single numeric vector (leave-one-values ) argument.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jackknife Estimation — jackknife","text":"f NULL (default), jackknife() returns named numeric vector following elements: original observed value applied object. mean jackknife estimate mean . bias jackknife estimate bias . error jackknife estimate standard error . f function, jackknife() returns result f applied leave-one-values .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jackknife Estimation — jackknife","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/jackknife.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jackknife Estimation — jackknife","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187 ## Jackknife resampling jackknife(h) #> original mean bias error #> Altamira 3.269200 3.246457 -0.9779158 0.08145529 #> Cueto de la Mina 2.955298 2.932856 -0.9649994 0.15907033 #> El Juyo 2.491683 2.469588 -0.9500688 0.19670002 #> El Cierro 2.485604 2.462723 -0.9838709 0.22195573 #> La Paloma 2.329187 2.305876 -1.0023704 0.25266291 jackknife(h, f = summary) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> Altamira 3.230811 3.236056 3.249552 3.246457 3.250979 3.269684 #> Cueto de la Mina 2.902193 2.911984 2.921893 2.932856 2.955298 3.018158 #> El Juyo 2.410614 2.444200 2.491683 2.469588 2.491683 2.529489 #> El Cierro 2.398698 2.425153 2.485604 2.462723 2.485604 2.485604 #> La Paloma 2.227438 2.260234 2.329187 2.305876 2.329187 2.329187"},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrigraph — matrigraph","title":"Matrigraph — matrigraph","text":"matrigraph() produces heatmap highlighting deviations independence. pvi() computes cell numeric matrix percentage column theoretical independence value.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrigraph — matrigraph","text":"","code":"matrigraph(object, ...) pvi(object, ...) # S4 method for class 'matrix' pvi(object) # S4 method for class 'data.frame' pvi(object) # S4 method for class 'matrix' matrigraph(object, reverse = FALSE, axes = TRUE, ...) # S4 method for class 'data.frame' matrigraph(object, reverse = FALSE, ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrigraph — matrigraph","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. reverse logical scalar: negative deviations centered (see details)? axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrigraph — matrigraph","text":"matrigraph() called side-effects: results graphic displayed (invisibly returns object). pvi() returns numeric matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Matrigraph — matrigraph","text":"PVI (french \"pourcentages de valeur d'indépendance\") calculated cell percentage column theoretical independence value: PVI greater \\(1\\) represent positive deviations independence, whereas PVI smaller \\(1\\) represent negative deviations (Desachy 2004). PVI matrix allows explore deviations independence (intuitive approach \\(\\chi^2\\)), way high-contrast matrix quite significant deviations, low risk due randomness (Desachy 2004). matrigraph() displays deviations independence: PVI equal \\(1\\) (statistical independence), cell matrix filled grey. PVI less \\(1\\) (negative deviation independence), size grey square proportional PVI (white margin thus represents fraction negative deviation). PVI greater \\(1\\) (positive deviation), black square representing fraction positive deviations superimposed. large positive deviations (PVI greater \\(2\\)), cell filled black. reverse TRUE, fraction negative deviations displayed white square.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Matrigraph — matrigraph","text":"Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Matrigraph — matrigraph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/matrigraph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrigraph — matrigraph","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\") ## Matrigraph matrigraph(compiegne) matrigraph(compiegne, reverse = TRUE) ## Compute PVI counts_pvi <- pvi(compiegne) plot_heatmap(counts_pvi, col = khroma::color(\"iridescent\")(12))"},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object — mutators","title":"Get or Set Parts of an Object — mutators","text":"Getters setters extract replace parts object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or Set Parts of an Object — mutators","text":"","code":"get_method(x) # S4 method for class 'DiversityIndex' labels(object, ...) # S4 method for class 'RarefactionIndex' labels(object, ...) # S4 method for class 'DiversityIndex' get_method(x)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object — mutators","text":"object, x R object get set element(s). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get or Set Parts of an Object — mutators","text":"labels() returns suitable set labels object use printing plotting.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/mutators.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get or Set Parts of an Object — mutators","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Co-Occurrence — occurrence","title":"Co-Occurrence — occurrence","text":"Co-Occurrence","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Co-Occurrence — occurrence","text":"","code":"occurrence(object, ...) # S4 method for class 'matrix' occurrence(object) # S4 method for class 'data.frame' occurrence(object)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Co-Occurrence — occurrence","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Co-Occurrence — occurrence","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Co-Occurrence — occurrence","text":"co-occurrence matrix symmetric matrix zeros main diagonal, works many times pairs taxa/types occur together least one sample.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Co-Occurrence — occurrence","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/occurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Co-Occurrence — occurrence","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Plot spot diagram of a co-occurrence matrix occ <- occurrence(cantabria) plot_spot(occ)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bertin Diagram — plot_bertin","title":"Bertin Diagram — plot_bertin","text":"Plots Bertin diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bertin Diagram — plot_bertin","text":"","code":"plot_bertin(object, ...) # S4 method for class 'matrix' plot_bertin( object, threshold = NULL, freq = FALSE, margin = 1, col = c(\"white\", \"black\"), flip = TRUE, axes = TRUE, ... ) # S4 method for class 'data.frame' plot_bertin( object, threshold = NULL, freq = FALSE, margin = 1, col = c(\"white\", \"black\"), flip = TRUE, axes = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bertin Diagram — plot_bertin","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. threshold function takes numeric vector argument returns numeric threshold value (see ). NULL (default), threshold computed. used freq FALSE. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. col vector colors. flip logical scalar: x y axis flipped? Defaults TRUE. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bertin Diagram — plot_bertin","text":"plot_bertin() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"bertin-matrix","dir":"Reference","previous_headings":"","what":"Bertin Matrix","title":"Bertin Diagram — plot_bertin","text":"de Falguerolles et al. (1997) points : \"abstract terms, Bertin matrix matrix displays. ... fix ideas, think data matrix, variable case, real valued variables. variable, draw bar chart variable value case. High-light bars representing value sample threshold variable.\"","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bertin Diagram — plot_bertin","text":"Bertin, J. (1977). La graphique et le traitement graphique de l'information. Paris: Flammarion. Nouvelle Bibliothèque Scientifique. de Falguerolles, ., Friedrich, F. & Sawitzki, G. (1997). Tribute J. Bertin's Graphical Data Analysis. W. Badilla & F. Faulbaum (eds.), SoftStat '97: Advances Statistical Software 6. Stuttgart: Lucius & Lucius, p. 11-20.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bertin Diagram — plot_bertin","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_bertin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bertin Diagram — plot_bertin","text":"","code":"## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\") ## Plot a Bertin diagram... ## ...without threshold plot_bertin(mississippi) ## ...with the variable mean as threshold plot_bertin(mississippi, threshold = mean) ## Plot conditional proportions plot_bertin(mississippi, freq = TRUE, margin = 1) plot_bertin(mississippi, freq = TRUE, margin = 2)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":null,"dir":"Reference","previous_headings":"","what":"Dice-Leraas Diagram — plot_diceleraas","title":"Dice-Leraas Diagram — plot_diceleraas","text":"Plots Dice-Leraas diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dice-Leraas Diagram — plot_diceleraas","text":"","code":"plot_diceleraas(object, ...) # S4 method for class 'matrix' plot_diceleraas( object, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = FALSE, panel.first = NULL, panel.last = NULL, ... ) # S4 method for class 'data.frame' plot_diceleraas( object, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = FALSE, panel.first = NULL, panel.last = NULL, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dice-Leraas Diagram — plot_diceleraas","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dice-Leraas Diagram — plot_diceleraas","text":"plot_diceleraas() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dice-Leraas Diagram — plot_diceleraas","text":"Dice-Leraas diagram, horizontal line represents range data (min-max) small vertical line indicates mean. black rectangle twice standard error mean, white rectangle one standard deviation either side mean.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dice-Leraas Diagram — plot_diceleraas","text":"Dice, L. R., & Leraas, H. J. (1936). Graphic Method Comparing Several Sets Measurements. Contributions Laboratory Vertebrate Genetics, 3: 1-3. Hubbs, C. L., & C. Hubbs (1953). Improved Graphical Analysis Comparison Series Samples. Systematic Biology, 2(2): 49-56. doi:10.2307/sysbio/2.2.49 . Simpson, G. G., Roe, ., & Lewontin, R. C. Quantitative Zoology. New York: Harcourt, Brace Company, 1960.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dice-Leraas Diagram — plot_diceleraas","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diceleraas.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dice-Leraas Diagram — plot_diceleraas","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\") ## Plot a Dice-Leraas diagram plot_diceleraas(compiegne)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Plot — plot_diversity","title":"Diversity Plot — plot_diversity","text":"Diversity Plot","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Plot — plot_diversity","text":"","code":"# S4 method for class 'DiversityIndex,missing' plot( x, log = \"x\", col.mean = \"#DDAA33\", col.interval = \"#004488\", lty.mean = \"solid\", lty.interval = \"dashed\", lwd.mean = 1, lwd.interval = 1, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Plot — plot_diversity","text":"x DiversityIndex object plotted. log character string indicating axes log scale. Defaults x. col.mean, col.interval character string specifying color lines. lty.mean, lty.interval character string numeric value specifying line types. lwd.mean, lwd.interval non-negative numeric value specifying line widths. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters passed graphics::points(), particularly, cex, col pch.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Plot — plot_diversity","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Plot — plot_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Plot — plot_diversity","text":"","code":"# \\donttest{ ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = \"shannon\") h_sim <- simulate(h) plot(h_sim) r <- richness(cantabria, method = \"count\") r_sim <- simulate(r) plot(r_sim) # }"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":null,"dir":"Reference","previous_headings":"","what":"Ford Diagram — plot_ford","title":"Ford Diagram — plot_ford","text":"Plots Ford (battleship curve) diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ford Diagram — plot_ford","text":"","code":"plot_ford(object, ...) # S4 method for class 'matrix' plot_ford( object, weights = FALSE, EPPM = FALSE, fill = \"darkgrey\", border = NA, axes = TRUE, ... ) # S4 method for class 'data.frame' plot_ford( object, weights = FALSE, EPPM = FALSE, fill = \"darkgrey\", border = NA, axes = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ford Diagram — plot_ford","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. weights logical scalar: row sums displayed? EPPM logical scalar: EPPM drawn? See seriograph(). fill color filling bars. border color draw borders. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ford Diagram — plot_ford","text":"plot_ford() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ford Diagram — plot_ford","text":"Ford, J. . (1962). quantitative method deriving cultural chronology. Washington, DC: Pan American Union. Technical manual 1.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ford Diagram — plot_ford","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_ford.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ford Diagram — plot_ford","text":"","code":"## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\") ## Plot a Ford diagram plot_ford(mississippi) plot_ford(mississippi, weights = TRUE)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap — plot_heatmap","title":"Heatmap — plot_heatmap","text":"Plots heatmap.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap — plot_heatmap","text":"","code":"plot_heatmap(object, ...) # S4 method for class 'matrix' plot_heatmap( object, col = color(\"YlOrBr\")(12), diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, fixed_ratio = TRUE, axes = TRUE, legend = TRUE, ... ) # S4 method for class 'data.frame' plot_heatmap( object, col = color(\"YlOrBr\")(12), diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, fixed_ratio = TRUE, axes = TRUE, legend = TRUE, ... ) # S4 method for class 'dist' plot_heatmap( object, col = color(\"YlOrBr\")(12), diag = FALSE, upper = FALSE, lower = !upper, axes = TRUE, legend = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap — plot_heatmap","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. col vector colors. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. fixed_ratio logical scalar: fixed aspect ratio (1) used? axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap — plot_heatmap","text":"plot_heatmap() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heatmap — plot_heatmap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap — plot_heatmap","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Plot raw data plot_heatmap(cantabria) ## Plot conditional proportions plot_heatmap(cantabria, freq = TRUE, margin = 1) plot_heatmap(cantabria, freq = TRUE, margin = 2)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix Plot — plot_matrix","title":"Matrix Plot — plot_matrix","text":"Matrix Plot","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix Plot — plot_matrix","text":"","code":"plot_matrix( object, panel, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, scale = TRUE, drop_zero = TRUE, col = graphics::par(\"fg\"), midpoint = NULL, axes = TRUE, legend = TRUE, asp = 1, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix Plot — plot_matrix","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). panel function form function(x, y, z, color, ...) gives action carried panel display. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. scale logical scalar indicating whether data rescaled \\([-1,1]\\). used freq FALSE. drop_zero logical scalar: zeros discarded? col vector colors. midpoint numeric value specifying data midpoint. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed? asp length-one numeric vector, giving aspect ratio \\(y/x\\). ... arguments passed panel.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Rank Plot — plot_rank","title":"Rank Plot — plot_rank","text":"Plots rank vs relative abundance diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rank Plot — plot_rank","text":"","code":"plot_rank(object, ...) # S4 method for class 'matrix' plot_rank( object, log = NULL, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topright\"), ... ) # S4 method for class 'data.frame' plot_rank( object, log = NULL, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topright\"), ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rank Plot — plot_rank","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters. log character string contains \"x\" x axis logarithmic, \"y\" y axis logarithmic \"xy\" \"yx\" axes logarithmic (base 10). main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rank Plot — plot_rank","text":"plot_rank() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rank Plot — plot_rank","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rank Plot — plot_rank","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rank Plot — plot_rank","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Plot rank vs abundance plot_rank(cantabria) ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot_rank(cantabria, col = col, pch = 15:19, lty = 2)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Plot — plot_rarefaction","title":"Rarefaction Plot — plot_rarefaction","text":"Rarefaction Plot","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction Plot — plot_rarefaction","text":"","code":"# S4 method for class 'RarefactionIndex,missing' plot( x, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topleft\"), ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction Plot — plot_rarefaction","text":"x RarefactionIndex object plotted. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed. ... graphical parameters passed graphics::lines().","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction Plot — plot_rarefaction","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Plot — plot_rarefaction","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction Plot — plot_rarefaction","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = \"baxter\") plot(rare, panel.first = graphics::grid()) ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot(rare, col = col, lty = 1:5)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":null,"dir":"Reference","previous_headings":"","what":"Spot Plot — plot_spot","title":"Spot Plot — plot_spot","text":"Plots spot matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spot Plot — plot_spot","text":"","code":"plot_spot(object, ...) # S4 method for class 'matrix' plot_spot( object, type = c(\"ring\", \"plain\"), col = color(\"YlOrBr\")(12), diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, axes = TRUE, legend = TRUE, ... ) # S4 method for class 'data.frame' plot_spot( object, type = c(\"ring\", \"plain\"), col = color(\"YlOrBr\")(12), diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, axes = TRUE, legend = TRUE, ... ) # S4 method for class 'dist' plot_spot( object, type = c(\"ring\", \"plain\"), col = color(\"YlOrBr\")(12), diag = FALSE, upper = FALSE, lower = !upper, axes = TRUE, legend = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spot Plot — plot_spot","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. type character string specifying graph plotted. must one \"ring\" (default) \"plain\". unambiguous substring can given. col vector colors. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spot Plot — plot_spot","text":"plot_spot() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spot Plot — plot_spot","text":"spot matrix can considered variant Bertin diagram data first transformed relative frequencies.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Spot Plot — plot_spot","text":"Adapted Dan Gopstein's original idea.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spot Plot — plot_spot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/plot_spot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spot Plot — plot_spot","text":"","code":"## Data from Huntley 2004, 2008 data(\"pueblo\") ## Plot spot diagram of count data plot_spot(pueblo, type = \"ring\") plot_spot(pueblo, type = \"plain\") ## Plot conditional proportions plot_spot(pueblo, freq = TRUE, margin = 1) plot_spot(pueblo, freq = TRUE, margin = 2)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Profiles — profiles","title":"Diversity Profiles — profiles","text":"Diversity Profiles","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Profiles — profiles","text":"","code":"profiles(object, ...) # S4 method for class 'matrix' profiles( object, alpha = seq(from = 0, to = 4, by = 0.04), main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topright\"), ... ) # S4 method for class 'data.frame' profiles( object, alpha = seq(from = 0, to = 4, by = 0.04), main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"topright\"), ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Profiles — profiles","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters passed graphics::lines() alpha numeric vector giving values alpha parameter. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Profiles — profiles","text":"profiles() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diversity Profiles — profiles","text":"profiles cross, diversities non-comparable across samples.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diversity Profiles — profiles","text":"Tóthmérész, B. (1995). Comparison Different Methods Diversity Ordering. Journal Vegetation Science, 6(2), 283-290. doi:10.2307/3236223 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Profiles — profiles","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/profiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Profiles — profiles","text":"","code":"## Replicate fig. 1 of Tóthmérész 1995 spc <- matrix( data = c(33, 29, 28, 5, 5, 0, 0, 42, 30, 10, 8, 5, 5, 0, 32, 21, 16, 12, 9, 6, 4), nrow = 3, byrow = TRUE, dimnames = list(c(\"A\", \"B\", \"C\"), NULL) ) profiles(spc)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/pueblo.html","id":null,"dir":"Reference","previous_headings":"","what":"Pueblo IV Period Ceramics — pueblo","title":"Pueblo IV Period Ceramics — pueblo","text":"dataset ceramic counts Zuni region.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/pueblo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pueblo IV Period Ceramics — pueblo","text":"","code":"pueblo"},{"path":"https://packages.tesselle.org/tabula/dev/reference/pueblo.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pueblo IV Period Ceramics — pueblo","text":"data.frame 9 rows 5 variables (compositional groups).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/pueblo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Pueblo IV Period Ceramics — pueblo","text":"Huntley, D. L. (2004). Interaction, Boundaries, Identities: Multiscalar Approach Organizational Scale Pueblo IV Zuni Society. Ph.D. Dissertation, Arizona State University. Huntley, D. L. (2022). Ancestral Zuni Glaze-Decorated Pottery: Viewing Pueblo IV Regional Organization Ceramic Production Exchange. Anthropological Papers University Arizona 72. Tucson: University Arizona Press. doi:10.2307/j.ctv2ngx5n8 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction — rarefaction","title":"Rarefaction — rarefaction","text":"Rarefaction","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction — rarefaction","text":"","code":"rarefaction(object, ...) # S4 method for class 'matrix' rarefaction(object, sample = NULL, method = c(\"hurlbert\", \"baxter\"), step = 1) # S4 method for class 'data.frame' rarefaction(object, sample = NULL, method = c(\"hurlbert\", \"baxter\"), step = 1)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction — rarefaction","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. sample length-one numeric vector giving sub-sample size. size sample smaller total community size. method character string vector strings specifying index computed (see details). unambiguous substring can given. step integer giving increment sample size.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction — rarefaction","text":"RarefactionIndex object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"rarefaction-measures","dir":"Reference","previous_headings":"","what":"Rarefaction Measures","title":"Rarefaction — rarefaction","text":"following rarefaction measures available count data: baxter Baxter's rarefaction. hurlbert Hurlbert's unbiased estimate Sander's rarefaction.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rarefaction — rarefaction","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (\\(S\\)) can used concept taxa number (McIntosh 1967). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(E(S)\\)) number taxa expected samples standard size (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction — rarefaction","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction — rarefaction","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = \"baxter\") plot(rare, panel.first = graphics::grid()) ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot(rare, col = col, lty = 1:5)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. arkhe remove_NA, remove_constant, remove_zero, replace_NA, replace_zero, sparsity khroma color, palette_color_continuous, palette_color_discrete, palette_color_picker","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample — resample","title":"Resample — resample","text":"Simulates observations multinomial distribution.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample — resample","text":"","code":"resample(object, ...) # S4 method for class 'numeric' resample(object, do, n, size = sum(object), ..., f = NULL)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample — resample","text":"object numeric vector count data (absolute frequencies). ... Extra arguments passed . function takes object argument returns single numeric value. n non-negative integer specifying number bootstrap replications. size non-negative integer specifying sample size. f function takes single numeric vector (result ) argument.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample — resample","text":"f NULL, resample() returns n values . Else, returns result f applied n values .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample — resample","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resample — resample","text":"","code":"## Sample observations from a multinomial distribution x <- sample(1:100, 50, TRUE) resample(x, do = median, n = 100) #> [1] 49.0 52.0 52.0 50.5 50.5 51.5 50.0 49.5 50.0 49.0 45.5 49.5 53.0 50.5 51.0 #> [16] 50.0 49.5 50.0 48.5 48.5 45.5 50.5 54.0 48.5 46.5 50.0 46.5 49.0 50.0 52.0 #> [31] 48.0 46.0 50.0 48.5 52.0 50.0 49.5 50.5 50.0 49.5 54.0 49.5 50.0 52.5 49.5 #> [46] 51.5 47.5 51.0 53.5 53.0 52.0 52.5 50.0 47.0 48.5 48.0 51.0 50.5 48.0 54.0 #> [61] 51.0 48.0 49.5 49.5 48.5 49.5 53.5 50.0 53.0 51.5 50.5 52.5 54.0 49.5 46.5 #> [76] 47.5 50.5 49.0 47.5 47.5 51.0 49.0 46.5 47.0 51.0 51.0 48.5 52.0 50.0 49.5 #> [91] 52.5 51.0 51.0 49.0 51.0 46.0 50.0 50.0 50.5 51.5 ## Estimate the 25th, 50th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.50, 0.75)) } resample(x, n = 100, do = median, f = quant) #> 25% 50% 75% #> 48.5 50.0 51.5"},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness — richness","title":"Richness — richness","text":"richness() computes sample richness. composition() computes asymptotic species richness.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Richness — richness","text":"","code":"richness(object, ...) composition(object, ...) # S4 method for class 'matrix' richness(object, ..., method = c(\"count\", \"margalef\", \"menhinick\")) # S4 method for class 'data.frame' richness(object, ..., method = c(\"count\", \"margalef\", \"menhinick\")) # S4 method for class 'matrix' composition(object, ..., method = c(\"chao1\", \"ace\", \"squares\", \"chao2\", \"ice\")) # S4 method for class 'data.frame' composition(object, ..., method = c(\"chao1\", \"ace\", \"squares\", \"chao2\", \"ice\"))"},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Richness — richness","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods (see ). method character string vector strings specifying index computed (see details). unambiguous substring can given.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Richness — richness","text":"richness() returns RichnessIndex object. composition() returns CompositionIndex object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Richness — richness","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (\\(S\\)) can used concept taxa number (McIntosh 1967). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(E(S)\\)) number taxa expected samples standard size (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"richness-measures","dir":"Reference","previous_headings":"","what":"Richness Measures","title":"Richness — richness","text":"following richness measures available count data: count Number observed taxa/types. margalef Margalef richness index. menhinick Menhinick richness index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"asymptotic-species-richness","dir":"Reference","previous_headings":"","what":"Asymptotic Species Richness","title":"Richness — richness","text":"following measures available count data: ace Abundance-based Coverage Estimator. chao1 (improved/unbiased) Chao1 estimator. squares Squares estimator. following measures available replicated incidence data: ice Incidence-based Coverage Estimator. chao2 (improved/unbiased) Chao2 estimator.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Richness — richness","text":"Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36. Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Magurran, E. & Brian J. McGill (2011). Biological Diversity: Frontiers Measurement Assessment. Oxford: Oxford University Press. McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674 . Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness — richness","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/richness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Richness — richness","text":"","code":"## Data from Magurran 1988, p. 128-129 trap <- matrix(data = c(9, 3, 0, 4, 2, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 2, 0, 5, 3, 0), nrow = 2, byrow = TRUE, dimnames = list(c(\"A\", \"B\"), NULL)) ## Margalef and Menhinick index richness(trap, method = \"margalef\") # 2.55 1.88 #> [1] 2.551432 1.949356 richness(trap, method = \"menhinick\") # 1.95 1.66 #> [1] 1.876630 1.664101 ## Data from Chao & Chiu 2016 brazil <- matrix( data = rep(x = c(1:21, 23, 25, 27, 28, 30, 32, 34:37, 41, 45, 46, 49, 52, 89, 110, 123, 140), times = c(113, 50, 39, 29, 15, 11, 13, 5, 6, 6, 3, 4, 3, 5, 2, 5, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0)), nrow = 1, byrow = TRUE ) ## Chao1-type estimators (asymptotic species richness) composition(brazil, method = c(\"chao1\"), unbiased = FALSE) # 461.625 #> [1] 461.6254 composition(brazil, method = c(\"ace\"), k = 10) # 445.822 #> [1] 443.6836"},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Seriograph — seriograph","title":"Seriograph — seriograph","text":"seriograph() produces Ford diagram highlighting relationships rows columns. eppm() computes cell numeric matrix positive difference column mean percentage.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seriograph — seriograph","text":"","code":"seriograph(object, ...) eppm(object, ...) # S4 method for class 'matrix' eppm(object) # S4 method for class 'data.frame' eppm(object) # S4 method for class 'matrix' seriograph( object, weights = FALSE, fill = \"darkgrey\", border = NA, axes = TRUE, ... ) # S4 method for class 'data.frame' seriograph( object, weights = FALSE, fill = \"darkgrey\", border = NA, axes = TRUE, ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seriograph — seriograph","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. weights logical scalar: row sums displayed? fill color filling bars. border color draw borders. axes logical scalar: axes drawn plot? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seriograph — seriograph","text":"seriograph() called side-effects: results graphic displayed (invisibly returns object). eppm() returns numeric matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Seriograph — seriograph","text":"positive difference column mean percentage (french \"écart positif au pourcentage moyen\", EPPM) represents deviation situation statistical independence. independence can interpreted absence relationships types chronological order assemblages, EPPM useful tool explore significance relationship rows columns related seriation (Desachy 2004). seriograph() superimposes frequencies (grey) EPPM values (black) row-column pair Ford diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Seriograph — seriograph","text":"Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Seriograph — seriograph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/seriograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seriograph — seriograph","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\") ## Seriograph seriograph(compiegne) seriograph(compiegne, weights = TRUE) ## Compute EPPM counts_eppm <- eppm(compiegne) plot_heatmap(counts_eppm, col = khroma::color(\"YlOrBr\")(12))"},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":null,"dir":"Reference","previous_headings":"","what":"SHE Analysis — she","title":"SHE Analysis — she","text":"Analysis","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHE Analysis — she","text":"","code":"she(object, ...) # S4 method for class 'matrix' she( object, unbiased = FALSE, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"right\"), ... ) # S4 method for class 'data.frame' she( object, unbiased = FALSE, main = NULL, sub = NULL, ann = graphics::par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = \"right\"), ... )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHE Analysis — she","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters passed graphics::lines() graphics::points(). unbiased logical scalar: bias-corrected estimator used (see index_shannon())? main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHE Analysis — she","text":"() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHE Analysis — she","text":"samples taken along gradient stratigraphic section, breaks curve may used infer discontinuities. assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHE Analysis — she","text":"Buzas, M. . & Hayek, L.-. C. (1998). analysis biofacies identification. Journal Foraminiferal Research, 1998, 28(3), 233-239. Hayek, L.-. C. & Buzas, M. . (2010). Surveying Natural Populations: Quantitative Tools Assessing Biodiversity. Second edition. New York: Columbia University Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"SHE Analysis — she","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/she.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHE Analysis — she","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## SHE analysis she(cantabria)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Similarity — similarity","title":"Similarity — similarity","text":"Similarity","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Similarity — similarity","text":"","code":"similarity(object, ...) # S4 method for class 'matrix' similarity( object, method = c(\"brainerd\", \"bray\", \"jaccard\", \"morisita\", \"sorenson\", \"binomial\") ) # S4 method for class 'data.frame' similarity( object, method = c(\"brainerd\", \"bray\", \"jaccard\", \"morisita\", \"sorenson\", \"binomial\") )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Similarity — similarity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. method character string specifying method used (see details). unambiguous substring can given.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Similarity — similarity","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Similarity — similarity","text":"\\(\\beta\\)-diversity can measured addressing similarity pairs samples/cases (Brainerd-Robinson, Jaccard, Morisita-Horn Sorenson indices). Similarity pairs taxa/types can measured assessing degree co-occurrence (binomial co-occurrence). Jaccard, Morisita-Horn Sorenson indices provide scale similarity \\(0\\)-\\(1\\) \\(1\\) perfect similarity \\(0\\) similarity. Brainerd-Robinson index scaled \\(0\\) \\(200\\). Binomial co-occurrence assessment approximates Z-score. binomial Binomial co-occurrence assessment. brainerd Brainerd-Robinson quantitative index. bray Sorenson quantitative index. jaccard Jaccard qualitative index. morisita Morisita-Horn quantitative index. sorenson Sorenson qualitative index.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Similarity — similarity","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Similarity — similarity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Similarity — similarity","text":"","code":"## Data from Huntley 2004, 2008 data(\"pueblo\") ## Brainerd-Robinson measure (C <- similarity(pueblo, \"brainerd\")) #> Atsinna Cienega Mirabal PdMuertos Hesh LowPesc BoxS #> Cienega 164.36782 #> Mirabal 152.38095 138.09524 #> PdMuertos 179.31034 150.57471 152.38095 #> Hesh 82.75862 55.55556 66.66667 103.44828 #> LowPesc 89.21023 62.00717 73.11828 109.89989 193.54839 #> BoxS 82.75862 55.55556 114.28571 102.17114 103.70370 103.70370 #> Ojo Bon 27.58621 22.22222 19.04762 20.68966 26.66667 25.80645 22.22222 #> S170 27.58621 22.22222 19.04762 20.68966 26.66667 25.80645 22.22222 #> Ojo Bon #> Cienega #> Mirabal #> PdMuertos #> Hesh #> LowPesc #> BoxS #> Ojo Bon #> S170 190.53030 plot_spot(C) ## Data from Magurran 1988, p. 166 data(\"aves\") ## Jaccard measure (presence/absence data) similarity(aves, \"jaccard\") # 0.46 #> unmanaged #> managed 0.4615385 ## Sorenson measure (presence/absence data) similarity(aves, \"sorenson\") # 0.63 #> unmanaged #> managed 0.6315789 # Jaccard measure (Bray's formula ; count data) similarity(aves, \"bray\") # 0.44 #> unmanaged #> managed 0.4442754 # Morisita-Horn measure (count data) similarity(aves, \"morisita\") # 0.81 #> unmanaged #> managed 0.8134497"},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Measure Diversity by Comparing to Simulated Assemblages — simulate","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"Measure Diversity Comparing Simulated Assemblages","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"","code":"# S4 method for class 'DiversityIndex' simulate( object, n = 1000, step = 1, interval = c(\"percentiles\", \"student\", \"normal\"), level = 0.8, progress = getOption(\"tabula.progress\") )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"object DiversityIndex object. n non-negative integer giving number bootstrap replications. step integer giving increment sample size. interval character string giving type confidence interval returned. must one \"percentiles\" (sample quantiles, described Kintigh 1984; default), \"student\" \"normal\". unambiguous substring can given. level length-one numeric vector giving confidence level. progress logical scalar: progress bar displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"Returns DiversityIndex object.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184 . Kintigh, K. W. (1984). Measuring Archaeological Diversity Comparison Simulated Assemblages. American Antiquity, 49(1), 44-54. doi:10.2307/280511 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/simulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"","code":"# \\donttest{ ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = \"shannon\") h_sim <- simulate(h) plot(h_sim) r <- richness(cantabria, method = \"count\") r_sim <- simulate(r) plot(r_sim) # }"},{"path":"https://packages.tesselle.org/tabula/dev/reference/tabula-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in tabula — tabula-deprecated","title":"Deprecated Functions in tabula — tabula-deprecated","text":"functions still work removed (defunct) next version.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/tabula-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions in tabula — tabula-deprecated","text":"","code":"test_diversity(object, ...) # S4 method for class 'matrix' test_diversity(object, adjust = \"holm\", ...) # S4 method for class 'data.frame' test_diversity(object, adjust = \"holm\", ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/tabula-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tabula: Analysis and Visualization of Archaeological Count Data — tabula-package","title":"tabula: Analysis and Visualization of Archaeological Count Data — tabula-package","text":"easy way examine archaeological count data. package provides several tests measures diversity: heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). allows easily visualize count data statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) Bertin (1977) diagrams, etc.","code":""},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/tabula-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tabula: Analysis and Visualization of Archaeological Count Data — tabula-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Archéosciences Bordeaux (UMR 6034) Maison de l'Archéologie Université Bordeaux Montaigne 33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Test — test","title":"Diversity Test — test","text":"Compares Shannon/Simpson diversity samples.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Test — test","text":"","code":"test_shannon(x, y, ...) test_simpson(x, y, ...) # S4 method for class 'numeric,numeric' test_shannon(x, y, ...) # S4 method for class 'matrix,missing' test_shannon(x, adjust = \"holm\", ...) # S4 method for class 'data.frame,missing' test_shannon(x, adjust = \"holm\", ...) # S4 method for class 'numeric,numeric' test_simpson(x, y, adjust = \"holm\", ...) # S4 method for class 'matrix,missing' test_simpson(x, adjust = \"holm\", ...) # S4 method for class 'data.frame,missing' test_simpson(x, adjust = \"holm\", ...)"},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Test — test","text":"x, y numeric vector, \\(m \\times p\\) matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. adjust character string specifying method adjusting \\(p\\) values (see stats::p.adjust()).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Test — test","text":"x y numeric vectors, returns list containing following components: statistic value t-statistic. parameter degrees freedom t-statistic. p.value p-value test. x matrix data.frame, returns table adjusted p-values lower triangular form.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Diversity Test — test","text":"test_shannon(x = matrix, y = missing): Produces two sided pairwise comparisons. test_shannon(x = data.frame, y = missing): Produces two sided pairwise comparisons. test_simpson(x = matrix, y = missing): Produces two sided pairwise comparisons. test_simpson(x = data.frame, y = missing): Produces two sided pairwise comparisons.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diversity Test — test","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Test — test","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Test — test","text":"","code":"## Data from Magurran 1988, p. 145-149 oakwood <- c(35, 26, 25, 21, 16, 11, 6, 5, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 0, 0) spruce <- c(30, 30, 3, 65, 20, 11, 0, 4, 2, 14, 0, 3, 9, 0, 0, 5, 0, 0, 0, 0, 1, 1) test_shannon(oakwood, spruce) #> $statistic #> [1] 3.534228 #> #> $parameter #> [1] 358.1946 #> #> $p.value #> [1] 0.0004625638 #> test_simpson(oakwood, spruce) #> $statistic #> [1] -2.871559 #> #> $parameter #> [1] 324.5595 #> #> $p.value #> [1] 0.004353068 #> ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\") test_shannon(cantabria) #> Altamira Cueto de la Mina El Juyo El Cierro #> Cueto de la Mina 8.268025e-02 NA NA NA #> El Juyo 3.754291e-05 0.06016855 NA NA #> El Cierro 3.526160e-05 0.05708787 1 NA #> La Paloma 3.255839e-05 0.01313679 1 1 test_simpson(cantabria) #> Altamira Cueto de la Mina El Juyo El Cierro #> Cueto de la Mina 0.84391773 NA NA NA #> El Juyo 0.05934092 0.6404647 NA NA #> El Cierro 0.32909278 1.0000000 1 NA #> La Paloma 0.37092662 1.0000000 1 1"},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":null,"dir":"Reference","previous_headings":"","what":"Turnover — turnover","title":"Turnover — turnover","text":"Returns degree turnover taxa composition along gradient transect.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turnover — turnover","text":"","code":"turnover(object, ...) # S4 method for class 'matrix' turnover( object, ..., method = c(\"whittaker\", \"cody\", \"routledge1\", \"routledge2\", \"routledge3\", \"wilson\") ) # S4 method for class 'data.frame' turnover( object, ..., method = c(\"whittaker\", \"cody\", \"routledge1\", \"routledge2\", \"routledge3\", \"wilson\") )"},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turnover — turnover","text":"object \\(m \\times p\\) numeric matrix data.frame count data incidence data. data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. method character string specifying method used (see details). unambiguous substring can given.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turnover — turnover","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Turnover — turnover","text":"following methods can used ascertain degree turnover taxa composition along gradient (\\(\\beta\\)-diversity) qualitative (presence/absence) data: cody Cody measure. routledge1 Routledge first measure. routledge2 Routledge second measure. routledge3 Routledge third measure (exponential form second measure). whittaker Whittaker measure. wilson Wilson measure. assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Turnover — turnover","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/turnover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turnover — turnover","text":"","code":"## Data from Magurran 1988, p. 162 data(\"woodland\") ## Whittaker's measure turnover(woodland, \"whittaker\") # 1 #> [1] 1 ## Cody's measure turnover(woodland, \"cody\") # 3 #> [1] 1 ## Routledge's measures turnover(woodland, \"routledge1\") # 0.29 #> [1] 1 turnover(woodland, \"routledge2\") # 0.56 #> [1] 1 turnover(woodland, \"routledge3\") # 1.75 #> [1] 1 ## Wilson and Shmida's measure turnover(woodland, \"wilson\") # 1 #> [1] 1"},{"path":"https://packages.tesselle.org/tabula/dev/reference/woodland.html","id":null,"dir":"Reference","previous_headings":"","what":"Trees Incidences — woodland","title":"Trees Incidences — woodland","text":"dataset presence absence trees six (10 x 10 m) quadarts along transect deciduous woodland.","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/woodland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trees Incidences — woodland","text":"","code":"woodland"},{"path":"https://packages.tesselle.org/tabula/dev/reference/woodland.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Trees Incidences — woodland","text":"data.frame 6 rows (quadarts) 6 variables (tree species).","code":""},{"path":"https://packages.tesselle.org/tabula/dev/reference/woodland.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Trees Incidences — woodland","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]}] diff --git a/tekton/pkgdown.yml b/tekton/pkgdown.yml index cd6935e60..221192f9b 100644 --- a/tekton/pkgdown.yml +++ b/tekton/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.0 pkgdown_sha: ~ articles: {} -last_built: 2024-07-26T12:31Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/tekton/reference article: https://packages.tesselle.org/tekton/articles diff --git a/tesselle/pkgdown.yml b/tesselle/pkgdown.yml index 8d2c60e92..5af0ba4e9 100644 --- a/tesselle/pkgdown.yml +++ b/tesselle/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.0 pkgdown_sha: ~ articles: {} -last_built: 2024-07-26T12:32Z +last_built: 2024-07-26T12:51Z urls: reference: https://packages.tesselle.org/tesselle/reference article: https://packages.tesselle.org/tesselle/articles