From d48c098f1caf6e7f895286ea6754c92085f92d0f Mon Sep 17 00:00:00 2001
From: dimensio <dimensio@tesselle.org>
Date: Mon, 4 Sep 2023 15:19:50 +0000
Subject: [PATCH] Built site for dimensio@83aa80a

---
 dimensio/dev/apple-touch-icon-120x120.png     | Bin 20346 -> 20346 bytes
 dimensio/dev/apple-touch-icon-152x152.png     | Bin 29702 -> 29702 bytes
 dimensio/dev/apple-touch-icon-180x180.png     | Bin 39742 -> 39742 bytes
 dimensio/dev/apple-touch-icon-60x60.png       | Bin 7245 -> 7245 bytes
 dimensio/dev/apple-touch-icon-76x76.png       | Bin 10287 -> 10287 bytes
 dimensio/dev/apple-touch-icon.png             | Bin 39742 -> 39742 bytes
 .../figure-html/biplot-columns-1.png          | Bin 80830 -> 81556 bytes
 .../pca_files/figure-html/biplot-rows-1.png   | Bin 82088 -> 83095 bytes
 .../pca_files/figure-html/plot-var-1.png      | Bin 63826 -> 63718 bytes
 .../pca_files/figure-html/plot-wrap-1.png     | Bin 121077 -> 121066 bytes
 dimensio/dev/favicon-16x16.png                | Bin 1455 -> 1455 bytes
 dimensio/dev/favicon-32x32.png                | Bin 2982 -> 2982 bytes
 dimensio/dev/pkgdown.yml                      |   2 +-
 dimensio/dev/reference/PCA-class.html         |  32 +++----
 dimensio/dev/reference/Rplot002.png           | Bin 22730 -> 22812 bytes
 dimensio/dev/reference/biplot-1.png           | Bin 44673 -> 44644 bytes
 dimensio/dev/reference/biplot-2.png           | Bin 61708 -> 62056 bytes
 dimensio/dev/reference/biplot-3.png           | Bin 50134 -> 50891 bytes
 dimensio/dev/reference/biplot-4.png           | Bin 53232 -> 52288 bytes
 dimensio/dev/reference/biplot.html            |   2 +-
 dimensio/dev/reference/bootstrap-1.png        | Bin 87420 -> 83028 bytes
 dimensio/dev/reference/bootstrap-2.png        | Bin 69456 -> 70494 bytes
 dimensio/dev/reference/bootstrap-3.png        | Bin 67128 -> 67315 bytes
 dimensio/dev/reference/bootstrap-4.png        | Bin 50968 -> 56912 bytes
 dimensio/dev/reference/pca.html               |  32 +++----
 dimensio/dev/reference/predict.html           |  84 +++++++++---------
 dimensio/dev/reference/viz_individuals-6.png  | Bin 57260 -> 57359 bytes
 dimensio/dev/reference/viz_variables-2.png    | Bin 64639 -> 64301 bytes
 dimensio/dev/reference/viz_variables.html     |  17 ++++
 dimensio/dev/search.json                      |   2 +-
 30 files changed, 94 insertions(+), 77 deletions(-)

diff --git a/dimensio/dev/apple-touch-icon-120x120.png b/dimensio/dev/apple-touch-icon-120x120.png
index f3ce3f8eeb424c80f72d08ebe25328c20627a924..5794475e3aa4340abe7918ac2b2e5be8b749ed8b 100644
GIT binary patch
delta 78
zcmex0kMY+$#tD_2oGko;=1cEyowu>c(NEk`*T5vi(A3J%+{)Bc+rYrez@W`$?f1#I
R{G_nSeNPnip3Lo^4*(La7@YtB

delta 78
zcmex0kMY+$#tD_295Nj2#=<LFL^n1$`iWcU8d-!GSXddDTbUYa8yHv_7@SoSsh)hx
RPYRpdp87S%Cv*Gf0|1<=7cKw*

diff --git a/dimensio/dev/apple-touch-icon-152x152.png b/dimensio/dev/apple-touch-icon-152x152.png
index 7e6ab90df9079e983387f88d8bc42577c4e3abfc..f8ead018a394dd06ea20263ea9021c3f37f63f41 100644
GIT binary patch
delta 99
zcmZpB!PxeKaY7|0Ckwxzg=xYH{*6s%i##lK4NO7|O|1;gtxV0d4GgRd47Oj{p2fhx
npjzS@QIe8al4_NkpOTqY$zWt)WUOm|MZ>X9{%ez`7v}>2`TrhP

delta 99
zcmZpB!PxeKaY7|0hYSb1N$(lKZ5x};7I|3c8d-!GSXddDTbUYZ8yHv_82mZK_l$vo
nLAAs+q9i4;B-JW6KP5A*lEKKp$XM3^i-z9m+oC2<FU|)5L^&Tm

diff --git a/dimensio/dev/apple-touch-icon-180x180.png b/dimensio/dev/apple-touch-icon-180x180.png
index ff63bc18b74bd0e952012ae231d004ff68110e72..4856a59d458d90f69751d5291170389e3e548aaf 100644
GIT binary patch
delta 78
zcmdnDjcMODrU{jtoGko;mQLw2W^Qa^oh5FmYhV&$Xli9>Ze?n&ZD3$!U?8}7&%Md>
RW=Ua_OZdl<H2M6jd;r7N7=r)+

delta 78
zcmdnDjcMODrU{jt95Nj2roT=rDr{_Goh5FeYh)2(U}0roZe?n$ZD3$!U=X#JHE8m@
RSyI^Kv>)t!H~IXmd;q#*7{>qr

diff --git a/dimensio/dev/apple-touch-icon-60x60.png b/dimensio/dev/apple-touch-icon-60x60.png
index 49e2c529bfa27559ebfd43f00a18d8aa825ed840..7bf7cd2aa553e6fb4ebf7291d273f5994cc93d4f 100644
GIT binary patch
delta 76
zcmX?Wan@o&B_}5fzo4o2#VeaOHi<}!Tk0B^gczDy8Jb&}8fzOESQ!{ReKX_i<h9aL
P*yN^cR4AN$PdXm}r>+<W

delta 76
zcmX?Wan@o&B`1dr2fI<q`Yp{Hn?$6=Ep&}6LJTad49u-e4YUmmtPBjYL*>s;UMnqy
OO-_nqdGX|X()j>c$Q70V

diff --git a/dimensio/dev/apple-touch-icon-76x76.png b/dimensio/dev/apple-touch-icon-76x76.png
index 1734002aaecc647400ba577ae9229ee8361afbf2..5e69a427aa464631742afb072e76513a6b12e8e6 100644
GIT binary patch
delta 76
zcmZ1<us&cyB_}5fzo6Nr8?780o4%=uTk0B^gczDy8Jb&}nrIssSQ!{7crSCF+^;T$
OO)gMO?*HTi>iGbAH5MoU

delta 76
zcmZ1<us&cyB`1dr2fI<q`Yp{Ho4%=uTj&~Dgcw*@8JJs{8fY6BSQ!{(hsvLy+^;T$
OO-_nqdGX`}>iGbPKNeE}

diff --git a/dimensio/dev/apple-touch-icon.png b/dimensio/dev/apple-touch-icon.png
index ff63bc18b74bd0e952012ae231d004ff68110e72..baab975378b10ce388e10cecfddcc7b4a1681c8d 100644
GIT binary patch
delta 78
zcmdnDjcMODrU{jtoGko;)@w3<Zr#|#I!oMA*T5vi(A3J%+{)Ba+rYrez`$|N@#e|%
RW=Ua_GdNgpKl%Kud;s`@88!d_

delta 78
zcmdnDjcMODrU{jt95Nj2roT=rDr{_Goh5FeYh)2(U}0roZe?n$ZD3$!U=X#JHE8m@
RSyI^Kv>)t!H~IXmd;q#*7{>qr

diff --git a/dimensio/dev/articles/pca_files/figure-html/biplot-columns-1.png b/dimensio/dev/articles/pca_files/figure-html/biplot-columns-1.png
index b8019c86614cb148b04319f453ebb8d94654d879..0ab9c429754d475b308ca06c2b22edd22af26fba 100644
GIT binary patch
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diff --git a/dimensio/dev/pkgdown.yml b/dimensio/dev/pkgdown.yml
index ebc735043..9b761163a 100644
--- a/dimensio/dev/pkgdown.yml
+++ b/dimensio/dev/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   pca: pca.html
-last_built: 2023-08-28T08:08Z
+last_built: 2023-09-04T15:18Z
 urls:
   reference: https://packages.tesselle.org/dimensio/reference
   article: https://packages.tesselle.org/dimensio/articles
diff --git a/dimensio/dev/reference/PCA-class.html b/dimensio/dev/reference/PCA-class.html
index d9c299a46..6015f083a 100644
--- a/dimensio/dev/reference/PCA-class.html
+++ b/dimensio/dev/reference/PCA-class.html
@@ -97,34 +97,34 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Get row coordinates</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="get_coordinates.html">get_coordinates</a></span><span class="op">(</span><span class="va">X</span>, margin <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          F1          F2  .sup</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 -2.058181  0.28490483 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 -2.192964  0.09003797 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4 -2.361881 -0.21625333 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5 -2.125573  0.18747140 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6 -1.605805  0.39770940 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7 -2.319919 -0.27102366 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 -1.974826  0.27679945 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 -2.107853  0.08452378 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3 -2.273786 -0.09394418 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 -2.274485 -0.21769741 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 -2.041339  0.18066161 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6 -1.526095  0.38607867 FALSE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get column coordinates</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="get_coordinates.html">get_coordinates</a></span><span class="op">(</span><span class="va">X</span>, margin <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                      F1         F2  .sup</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Length  0.9346494  0.3543710 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Length  0.9852172 -0.1086968 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Width   0.9668940 -0.2317965 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Width  -0.3160646  0.4483048  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Length  0.9347789  0.3540256 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Length  0.9853020 -0.1083268 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Width   0.9669170 -0.2318721 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Width  -0.3178714  0.4453379  TRUE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get correlations between variables and dimensions</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="get_contributions.html">get_correlations</a></span><span class="op">(</span><span class="va">X</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                      F1         F2  .sup</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Length  0.9346494  0.3543710 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Length  0.9852172 -0.1086968 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Width   0.9668940 -0.2317965 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Width  -0.3160646  0.4483048  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Length  0.9347789  0.3540256 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Length  0.9853020 -0.1083268 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Width   0.9669170 -0.2318721 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Width  -0.3178714  0.4453379  TRUE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get eigenvalues</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="get_eigenvalues.html">get_eigenvalues</a></span><span class="op">(</span><span class="va">X</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    eigenvalues  variance cumulative</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F1   2.7791065 93.565365   93.56536</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F2   0.1911235  6.434635  100.00000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> F1   2.7795601 93.575482   93.57548</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> F2   0.1908335  6.424518  100.00000</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/dimensio/dev/reference/biplot.html b/dimensio/dev/reference/biplot.html
index 39095bd85..6eded278f 100644
--- a/dimensio/dev/reference/biplot.html
+++ b/dimensio/dev/reference/biplot.html
@@ -167,7 +167,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>...</dt>
-<dd><p>Further parameters to be passed to <code><a href="https://rdrr.io/pkg/wordcloud/man/wordlayout.html" class="external-link">wordcloud::wordlayout()</a></code>.</p></dd>
+<dd><p>Currently not used.</p></dd>
 
 
 <dt>lty, lwd</dt>
diff --git a/dimensio/dev/reference/bootstrap-1.png b/dimensio/dev/reference/bootstrap-1.png
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diff --git a/dimensio/dev/reference/pca.html b/dimensio/dev/reference/pca.html
index 59d5b7292..ebb4b6304 100644
--- a/dimensio/dev/reference/pca.html
+++ b/dimensio/dev/reference/pca.html
@@ -172,34 +172,34 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Get row coordinates</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="get_coordinates.html">get_coordinates</a></span><span class="op">(</span><span class="va">X</span>, margin <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          F1          F2  .sup</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 -2.037750  0.28449650 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 -2.173723  0.08693737 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3 -2.343172 -0.09618534 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4 -2.344208 -0.22383774 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5 -2.105737  0.18571694 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6 -1.582347  0.40133551 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 -2.035960  0.31581917 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 -2.169988  0.12171682 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3 -2.337513 -0.05789049 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 -2.337532 -0.18393173 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 -2.102974  0.21876799 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6 -1.582823  0.42642756 FALSE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get column coordinates</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="get_coordinates.html">get_coordinates</a></span><span class="op">(</span><span class="va">X</span>, margin <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                      F1         F2  .sup</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Length  0.9310079  0.3638965 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Length  0.9843567 -0.1142004 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Width   0.9659776 -0.2343497 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Width  -0.3174565  0.4886782  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Length  0.9345943  0.3545602 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Length  0.9844440 -0.1115899 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Width   0.9671282 -0.2290451 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Width  -0.3006341  0.4661255  TRUE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get correlations between variables and dimensions</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="get_contributions.html">get_correlations</a></span><span class="op">(</span><span class="va">X</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                      F1         F2  .sup</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Length  0.9310079  0.3638965 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Length  0.9843567 -0.1142004 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Width   0.9659776 -0.2343497 FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Width  -0.3174565  0.4886782  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Length  0.9345943  0.3545602 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Length  0.9844440 -0.1115899 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Petal.Width   0.9671282 -0.2290451 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Sepal.Width  -0.3006341  0.4661255  TRUE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get eigenvalues</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="get_eigenvalues.html">get_eigenvalues</a></span><span class="op">(</span><span class="va">X</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    eigenvalues  variance cumulative</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F1   2.7688465 93.251372   93.25137</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F2   0.2003822  6.748628  100.00000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> F1   2.7779334 93.578472   93.57847</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> F2   0.1906269  6.421528  100.00000</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/dimensio/dev/reference/predict.html b/dimensio/dev/reference/predict.html
index af36b9d5d..cd47bb7bf 100644
--- a/dimensio/dev/reference/predict.html
+++ b/dimensio/dev/reference/predict.html
@@ -109,52 +109,52 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Predict new row coordinates</span></span></span>
 <span class="r-in"><span><span class="va">Y</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span>data <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, <span class="fl">120</span>, <span class="cn">TRUE</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">20</span>, ncol <span class="op">=</span> <span class="fl">6</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">predict</span><span class="op">(</span><span class="va">X</span>, <span class="va">Y</span>, margin <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             F1          F2          F3          F4          F5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  -0.11393136  0.12196021 -0.03933027  0.07547897  0.22868955</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2  -0.36245566  0.34426865 -0.47883474  0.21197807 -0.16705579</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   0.10458280  0.22830858  0.53864849  0.01975142 -0.30258976</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4  -0.29542894  0.14813156  0.13537729  0.17444654 -0.28157716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  -0.02465857  0.05552166  0.17514484  0.15150058 -0.09443768</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   0.10813085  0.21997827 -0.19071388  0.19689191 -0.54721259</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  -0.37242363 -0.01568988  0.18957832  0.04289928 -0.19863331</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   0.17257300  0.17911758  0.08824491 -0.06422399  0.44277505</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   0.07707648  0.19986807  0.25371803  0.18164763 -0.22047288</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 -0.07236992  0.18254891  0.04612638  0.48076918  0.13052350</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11  0.40533664  0.06978921 -0.18065696  0.46788388 -0.04925436</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12 -0.16644791  0.17671218 -0.01710058 -0.08502750 -0.14945800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13 -0.09970503  0.12432823 -0.13474191  0.57986567  0.59065788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14  0.12139432 -0.21341644  0.15966991  0.10509173  0.09162720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15 -0.19982542 -0.06666130  0.10155244  0.42434594 -0.20052918</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16  0.14729423  0.21361720 -0.26987112  0.15856722 -0.42553311</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17  0.02571276  0.15473673  0.10631792  0.08367243 -0.36746158</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18  0.47560029  0.06889942  0.11271582  0.26929808  0.03044542</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19 -0.02630321  0.12540419 -0.37097389  0.29947778  0.33343898</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20  0.15506270  0.24345068  0.32268311 -0.18034512 -0.06256898</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>             F1          F2            F3           F4           F5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1   0.17471703 -0.07345523 -0.2675224180 -0.016461985  0.131859566</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   0.56372762  0.16957306 -0.1007627536 -0.045990757  0.411294332</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3  -0.22449955  0.09186010  0.0369589806 -0.104655765  0.002451712</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   0.63896235  0.29815571 -0.0852431293  0.015201142 -0.451513855</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5  -0.01845217 -0.17855777 -0.0741434899  0.298677225  0.438098455</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  -0.03479994 -0.17279067 -0.0776743260 -0.136296505  0.138266904</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   0.47801479 -0.17460920  0.4123742862 -0.200470446 -0.121118638</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8  -0.08234559 -0.20504704  0.4427467232 -0.278209716  0.087579646</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   0.21324015  0.03726549 -0.2104299068  0.240673400 -0.066782483</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  0.24811728 -0.33282164  0.1602755166 -0.003335075 -0.215721088</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11  0.03208209  0.30410017 -0.2382955604  0.045746891  0.248711152</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12  0.27969423  0.08527592  0.3985796748  0.401317525  0.153434400</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13  0.07625653 -0.04916791  0.2464073168 -0.223718176  0.069195721</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14  0.40027196  0.01657354  0.2767866947 -0.316098142 -0.110760529</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15  0.25281186  0.06561033  0.1618805607  0.029036797  0.135733024</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16 -0.10415927 -0.04558003  0.0390609697  0.529081002 -0.047924800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17 -0.11226220  0.04858745 -0.0002276991 -0.103555348 -0.169071476</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18  0.19181059 -0.03719919 -0.2290754735 -0.003189704 -0.020388346</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19  0.34062276  0.32012843  0.3501265861  0.630473546 -0.008598229</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20 -0.04408791  0.25860195 -0.2498443398  0.142807006  0.115622270</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Predict new column coordinates</span></span></span>
 <span class="r-in"><span><span class="va">Z</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span>data <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, <span class="fl">140</span>, <span class="cn">TRUE</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">7</span>, ncol <span class="op">=</span> <span class="fl">20</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">predict</span><span class="op">(</span><span class="va">X</span>, <span class="va">Z</span>, margin <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              F1          F2          F3          F4           F5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  -0.014348453  0.02428119 -0.23553827 -0.04363336  0.095996772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   0.253798662  0.07319638  0.41986009  0.24987843 -0.005412720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   0.127891431 -0.07966325  0.08669107 -0.02128859 -0.170614788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   0.117044566  0.22911437  0.23148916  0.04901558 -0.258025402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  -0.068236532 -0.33260007  0.18317602 -0.22303068 -0.035566346</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  -0.002347086 -0.08444443 -0.12251044 -0.09982256 -0.157513109</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  -0.198671358  0.17230814  0.25896714 -0.04459512 -0.080602364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  -0.385907223 -0.18988897  0.29818477 -0.01087143  0.063660402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  -0.074609791  0.04924708 -0.47131970  0.07851356 -0.092664017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 -0.154511317  0.22949038  0.43205684 -0.21288387 -0.021507839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11 -0.304891966  0.03488418  0.46609342  0.01606043  0.246270360</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12 -0.127321998  0.14709106  0.08247683 -0.25801248  0.174759416</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13 -0.246324025  0.13786864  0.02890378  0.11672619  0.166996621</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14 -0.223390576  0.13350105  0.13369598 -0.09144535  0.216573789</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15 -0.131721425 -0.12198161  0.41155539 -0.24856352 -0.236235957</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16  0.093521292 -0.12964924  0.34384056  0.02304465  0.108082767</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17 -0.025465795 -0.02249537  0.05746759 -0.29993442  0.005918782</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18 -0.138221744  0.35583543 -0.13878305  0.18888950  0.028078939</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19 -0.254076079 -0.03922454 -0.29123610  0.31611310 -0.214836059</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20 -0.167505264  0.17521753  0.05120108 -0.38165447  0.015001565</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>             F1           F2           F3           F4           F5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  -0.37468727  0.267544239  0.379431411  0.092072423 -0.063395536</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2  -0.35514173  0.201801672  0.187809125  0.081584050  0.276498726</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   0.34075490  0.029827461  0.018081686 -0.081435548  0.069575617</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   0.56008251 -0.059289422 -0.326660405 -0.085300260 -0.271063174</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   0.18090639 -0.006662934 -0.004062043 -0.056018727 -0.103853822</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  -0.17318505 -0.227625496  0.268051373  0.049581840  0.072724542</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   0.18830472 -0.575673823  0.094792921 -0.190411571  0.030318070</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8  -0.49497642 -0.094899901 -0.187268910 -0.107039912  0.222990309</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9  -0.10671904 -0.074753812 -0.215441336  0.043540684 -0.180243373</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  0.13819071  0.200383814  0.251182019  0.398210599  0.351916770</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11  0.18294267  0.100883980  0.100368169  0.140975766 -0.043048524</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12 -0.37689643  0.038547535 -0.026556652 -0.021784260 -0.372157907</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13 -0.26041527  0.105391410  0.332961308  0.027724387 -0.006379742</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14  0.15164241 -0.142508621 -0.180439248 -0.257154858 -0.120254237</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15 -0.01429101  0.304113288  0.021080097 -0.171892459 -0.289524670</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16 -0.41145741 -0.160076170 -0.470983963 -0.105484715  0.292064565</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17 -0.19878412 -0.046358033  0.068842960 -0.003079981  0.159552838</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18 -0.25334874  0.078506894  0.270100053 -0.102226801 -0.400739267</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19 -0.09957540 -0.080938591  0.202546040 -0.087392983  0.125346702</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20 -0.32567757 -0.220010893  0.153607703 -0.103977758 -0.235926062</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/dimensio/dev/reference/viz_individuals-6.png b/dimensio/dev/reference/viz_individuals-6.png
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diff --git a/dimensio/dev/reference/viz_variables.html b/dimensio/dev/reference/viz_variables.html
index 93e9790be..d9e109bb2 100644
--- a/dimensio/dev/reference/viz_variables.html
+++ b/dimensio/dev/reference/viz_variables.html
@@ -98,6 +98,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
+<span><span class="co"># S4 method for CA</span></span>
+<span><span class="fu">viz_variables</span><span class="op">(</span></span>
+<span>  <span class="va">x</span>,</span>
+<span>  axes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>,</span>
+<span>  active <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  sup <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  labels <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  highlight <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  xlim <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  ylim <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  main <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  sub <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  panel.first <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  panel.last <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span>
+<span></span>
 <span><span class="co"># S4 method for BootstrapPCA</span></span>
 <span><span class="fu">viz_variables</span><span class="op">(</span><span class="va">x</span>, axes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
diff --git a/dimensio/dev/search.json b/dimensio/dev/search.json
index 40fb35750..112202e0a 100644
--- a/dimensio/dev/search.json
+++ b/dimensio/dev/search.json
@@ -1 +1 @@
-[{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"do-pca","dir":"Articles","previous_headings":"","what":"Do PCA","title":"Principal Components Analysis","text":"","code":"## Load data data(iris) head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa  ## Compute PCA ## (non numeric variables are automatically removed) X <- pca(iris, center = TRUE, scale = TRUE) #> 1 qualitative variable was removed: Species."},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"explore-the-results","dir":"Articles","previous_headings":"","what":"Explore the results","title":"Principal Components Analysis","text":"dimensio provides several methods extract (get_*()) results: get_data() returns original data. get_contributions() returns contributions definition principal dimensions. get_coordinates() returns principal standard coordinates. get_correlations() returns correlations variables dimensions. get_cos2() returns cos2 values (.e. quality representation points factor map). get_eigenvalues() returns eigenvalues, percentages variance cumulative percentages variance. package also allows quickly visualize (viz_*()) results: biplot() produces biplot. screeplot() produces scree plot. viz_rows()/viz_individuals() displays row/individual principal coordinates. viz_columns()/viz_variables() displays columns/variable principal coordinates. viz_contributions() displays (joint) contributions. viz_cos2() displays (joint) cos2.","code":"## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.9184978 73.342264   73.34226 #> F2   0.9140305 22.969715   96.31198 #> F3   0.1467569  3.688021  100.00000  ## Scree plot screeplot(X, cumulative = TRUE)  ## Plot variable contributions to the definition of the first two axes viz_contributions(X, margin = 2, axes = c(1, 2))"},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"pca-biplot","dir":"Articles","previous_headings":"","what":"PCA biplot","title":"Principal Components Analysis","text":"biplot simultaneous representation rows columns rectangular dataset. generalization scatterplot case mutlivariate data: allows visualize much information possible single graph (Greenacre 2010). dimensio allows display two types biplots: form biplot (row-metric-preserving biplot) covariance biplot (column-metric-preserving biplot). See Greenacre (2010) details biplots. form biplot favors representation individuals: distance individuals approximates Euclidean distance rows. form biplot length vector approximates quality representation variable.  covariance biplot favors representation variables: length vector approximates standard deviation variable cosine angle formed two vectors approximates correlation two variables (Greenacre 2010). covariance biplot distance individuals approximates Mahalanobis distance rows.  Biplots drawbacks advantages: can quickly become difficult read display lot information . may preferable visualize results individuals variables separately.","code":"biplot(X, type = \"form\", label = \"variables\") biplot(X, type = \"covariance\", label = \"variables\")"},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"plot-pca-loadings","dir":"Articles","previous_headings":"","what":"Plot PCA loadings","title":"Principal Components Analysis","text":"viz_variables() depicts variables rays emanating origin (lengths directions important interpretation).  viz_variables() allows highlight additional information varying different graphical elements (color, transparency, shape size symbols…).","code":"## Plot variables factor map viz_variables(X) ## Highlight cos2 viz_variables(   x = X,    highlight = \"cos2\",    col = khroma::color(\"YlOrBr\")(4, range = c(0.5, 1)) )"},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"plot-pca-scores","dir":"Articles","previous_headings":"","what":"Plot PCA scores","title":"Principal Components Analysis","text":"viz_individuals() allows display individuals highlight additional information.","code":"## Plot individuals and color by species viz_individuals(   x = X,   highlight = iris$Species,   col = khroma::color(\"bright\")(3), # Custom color scale   pch = c(15, 16, 17) # Custom symbols ) ## Add ellipses viz_individuals(x = X) viz_tolerance(x = X, group = iris$Species, level = 0.95,               border = khroma::color(\"high contrast\")(3))  ## Add convex hull viz_individuals(x = X) viz_hull(x = X, group = iris$Species, level = 0.95,          border = khroma::color(\"high contrast\")(3)) ## Highlight petal length viz_individuals(   x = X,    highlight = iris$Petal.Length,   col = khroma::color(\"YlOrBr\")(12), # Custom color scale   cex = c(1, 2), # Custom size scale   pch = 16 ) ## Highlight contributions viz_individuals(   x = X,    highlight = \"contrib\",   col = khroma::color(\"iridescent\")(12), # Custom color scale   cex = c(1, 2), # Custom size scale   pch = 16 )"},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"custom-plot","dir":"Articles","previous_headings":"","what":"Custom plot","title":"Principal Components Analysis","text":"need flexibility, get_*() family tidy() augment() functions allow extract results data frames thus build custom graphs base graphics ggplot2.","code":"iris_tidy <- tidy(X, margin = 2) head(iris_tidy) #>          label component supplementary coordinate contribution        cos2 #> 1 Petal.Length        F1         FALSE 0.99155518  33.68793618 0.983181682 #> 2 Petal.Length        F2         FALSE 0.02341519   0.05998389 0.000548271 #> 3 Petal.Length        F3         FALSE 0.05444699   2.01999049 0.002964475 #> 4  Petal.Width        F1         FALSE 0.96497896  31.90629060 0.931184395 #> 5  Petal.Width        F2         FALSE 0.06399985   0.44812296 0.004095980 #> 6  Petal.Width        F3         FALSE 0.24298265  40.23019050 0.059040571  iris_augment <- augment(X, margin = 1) head(iris_augment) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.264703  0.4800266     1         FALSE 0.006666667 5.359304     3.572870 #> 2 -2.080961 -0.6741336     2         FALSE 0.006666667 4.784855     3.189904 #> 3 -2.364229 -0.3419080     3         FALSE 0.006666667 5.706480     3.804320 #> 4 -2.299384 -0.5973945     4         FALSE 0.006666667 5.644048     3.762699 #> 5 -2.389842  0.6468354     5         FALSE 0.006666667 6.129742     4.086494 #> 6 -2.075631  1.4891775     6         FALSE 0.006666667 6.525894     4.350596 #>        cos2 #> 1 0.9968578 #> 2 0.9864650 #> 3 0.9995167 #> 4 0.9977577 #> 5 0.9997491 #> 6 0.9998819 ## Custom plot with ggplot2 ggplot2::ggplot(data = iris_augment) +   ggplot2::aes(x = F1, y = F2, colour = contribution) +   ggplot2::geom_vline(xintercept = 0, linewidth = 0.5, linetype = \"dashed\") +   ggplot2::geom_hline(yintercept = 0, linewidth = 0.5, linetype = \"dashed\") +   ggplot2::geom_point() +   ggplot2::coord_fixed() + # /!\\   ggplot2::theme_bw() +   khroma::scale_color_iridescent()"},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Multivariate Data Analysis","text":"Simple Principal Components Analysis (PCA; see vignette(\"pca\")) Correspondence Analysis (CA) based Singular Value Decomposition (SVD). package provides S4 classes methods compute, extract, summarize visualize results multivariate data analysis. also includes methods partial bootstrap validation. many good packages multivariate data analysis (FactoMineR, ade4, vegan ca, extended FactoExtra). dimensio designed simple possible, providing necessary tools explore results analysis.","code":"To cite dimensio in publications use:    Frerebeau N (2023). _dimensio: Multivariate Data Analysis_.   Université Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.4478530 <https://doi.org/10.5281/zenodo.4478530>,   R package version 0.4.0, <https://packages.tesselle.org/dimensio/>.  Une entrée BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{dimensio: Multivariate Data Analysis}},     year = {2023},     organization = {Université Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.4.0},     doi = {10.5281/zenodo.4478530},     url = {https://packages.tesselle.org/dimensio/},   }  This package is a part of the tesselle project <https://www.tesselle.org>."},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Multivariate Data Analysis","text":"can install released version dimensio CRAN : development version GitHub :","code":"install.packages(\"dimensio\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/dimensio\")"},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Multivariate Data Analysis","text":"","code":"## Install extra packages (if needed) # install.packages(\"khroma\")  ## Load packages library(dimensio)"},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"compute","dir":"","previous_headings":"Usage","what":"Compute","title":"Multivariate Data Analysis","text":"","code":"## Load data data(iris)  ## Compute PCA ## (non numeric variables are automatically removed) X <- pca(iris, center = TRUE, scale = TRUE) #> 1 qualitative variable was removed: Species."},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"extract","dir":"","previous_headings":"Usage","what":"Extract","title":"Multivariate Data Analysis","text":"dimensio provides several methods extract results: get_data() returns original data. get_contributions() returns contributions definition principal dimensions. get_coordinates() returns principal standard coordinates. get_correlations() returns correlations variables dimensions. get_cos2() returns cos2 values (.e. quality representation points factor map). get_eigenvalues() returns eigenvalues, percentages variance cumulative percentages variance.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"visualize","dir":"","previous_headings":"Usage","what":"Visualize","title":"Multivariate Data Analysis","text":"package allows quickly visualize results: biplot() produces biplot. screeplot() produces scree plot. viz_rows()/viz_individuals() displays row/individual principal coordinates. viz_columns()/viz_variables() displays columns/variable principal coordinates. viz_variables() depicts variables rays emanating origin (lengths directions important interpretation). viz_contributions() displays (joint) contributions. viz_cos2() displays (joint) cos2. viz_*() functions allow highlight additional information varying different graphical elements (color, transparency, shape size symbols…).","code":"## Form biplot biplot(X, type = \"form\") ## Highlight species viz_individuals(   x = X,    highlight = iris$Species,    col = khroma::color(\"high contrast\")(3),    pch = 16 ) ## Add ellipses viz_tolerance(   x = X,    group = iris$Species,    level = 0.95,   border = khroma::color(\"high contrast\")(3) )  ## Highlight petal length viz_individuals(   x = X,   highlight = iris$Petal.Length,   col = khroma::color(\"iridescent\")(255),    cex = c(1, 2),   pch = 16 ) ## Plot variables factor map viz_variables(X)  ## Scree plot screeplot(X, eigenvalues = FALSE, cumulative = TRUE)"},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Multivariate Data Analysis","text":"Please note dimensio project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/CA-class.html","id":null,"dir":"Reference","previous_headings":"","what":"CA Results — CA-class","title":"CA Results — CA-class","text":"S4 class store results simple correspondence analysis.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/CA-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"CA Results — CA-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/CA-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CA Results — CA-class","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## The chi square of independence between the two variables stats::chisq.test(colours) #>  #> \tPearson's Chi-squared test #>  #> data:  colours #> X-squared = 138.29, df = 9, p-value < 2.2e-16 #>   ## Compute correspondence analysis X <- ca(colours)  ## Plot rows viz_rows(X, labels = TRUE)   ## Plot columns viz_columns(X, labels = TRUE)   ## Get row coordinates head(get_coordinates(X, margin = 1)) #>                  F1          F2           F3  .sup #> marron   -0.4921577 -0.08832151  0.021611305 FALSE #> noisette -0.2125969  0.16739109 -0.100518284 FALSE #> vert      0.1617534  0.33903957  0.087597437 FALSE #> bleu      0.5474139 -0.08295428 -0.004709408 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                 F1          F2          F3  .sup #> brun    -0.5045624 -0.21482046  0.05550909 FALSE #> chatain -0.1482527  0.03266635 -0.04880414 FALSE #> roux    -0.1295233  0.31964240  0.08315117 FALSE #> blond    0.8353478 -0.06957934  0.01621471 FALSE  ## Get row distances to centroid head(get_distances(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.25048691 0.08332116 0.14878530 0.30656555   ## Get row inertias head(get_inertia(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.09308635 0.01308930 0.01608490 0.11133715   ## Get row contributions head(get_contributions(X, margin = 1)) #>                 F1       F2         F3 #> marron   43.115744 13.04249  6.6795992 #> noisette  3.400961 19.80398 61.0855951 #> vert      1.354851 55.90952 31.9248234 #> bleu     52.128445 11.24401  0.3099822  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1 0.208772652 89.372732   89.37273 #> F2 0.022226615  9.514911   98.88764 #> F3 0.002598439  1.112356  100.00000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateAnalysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Output of Multivariate Data Analysis — MultivariateAnalysis","title":"Output of Multivariate Data Analysis — MultivariateAnalysis","text":"virtual S4 class store output multivariate data analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateAnalysis.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Output of Multivariate Data Analysis — MultivariateAnalysis","text":"data numeric matrix. dimension integer giving dimension solution. singular_values numeric vector giving singular values. rows MultivariateResults object. columns MultivariateResults object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateAnalysis.html","id":"subset","dir":"Reference","previous_headings":"","what":"Subset","title":"Output of Multivariate Data Analysis — MultivariateAnalysis","text":"code snippets , x MultivariateAnalysis object. x[[]] Extracts information slot selected subscript . length-one character vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateAnalysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Output of Multivariate Data Analysis — MultivariateAnalysis","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateBootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Output of Bootstrap Replications — MultivariateBootstrap","title":"Output of Bootstrap Replications — MultivariateBootstrap","text":"virtual S4 class store output bootstrap analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateBootstrap.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Output of Bootstrap Replications — MultivariateBootstrap","text":"replications integer giving number bootstrap replications.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateBootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Output of Bootstrap Replications — MultivariateBootstrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateResults.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Data Analysis Results — MultivariateResults","title":"Multivariate Data Analysis Results — MultivariateResults","text":"S4 class store results multivariate data analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateResults.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Multivariate Data Analysis Results — MultivariateResults","text":"names character vector specifying row names. principal numeric matrix giving principal coordinates. standard numeric matrix giving standard coordinates. contributions numeric matrix giving contributions definition dimensions. cosine numeric matrix giving \\(cos^2\\) values. distances numeric vector giving distances centroid. weights numeric vector giving masses/weights. supplement logical vector specifying supplementary points. order integer vector giving original indices data (computation moves supplementary points end results). groups character vector specifying class observation.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateResults.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Multivariate Data Analysis Results — MultivariateResults","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateSummary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of Multivariate Data Analysis — MultivariateSummary","title":"Summary of Multivariate Data Analysis — MultivariateSummary","text":"virtual S4 class store summary multivariate data analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateSummary.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Summary of Multivariate Data Analysis — MultivariateSummary","text":"data numeric matrix. eigenvalues numeric matrix. results numeric matrix. supplement logical vector specifying supplementary points. margin integer.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateSummary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary of Multivariate Data Analysis — MultivariateSummary","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/PCA-class.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Results — PCA-class","title":"PCA Results — PCA-class","text":"S4 class store results principal components analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/PCA-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"PCA Results — PCA-class","text":"center numeric vector giving column mean initial dataset (active individuals ). scale numeric vector giving column standard deviations initial dataset (active individuals ).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/PCA-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"PCA Results — PCA-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/PCA-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Results — PCA-class","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2) #> 1 qualitative variable was removed: Species.  ## Get row coordinates head(get_coordinates(X, margin = 1)) #>          F1          F2  .sup #> 1 -2.058181  0.28490483 FALSE #> 2 -2.192964  0.09003797 FALSE #> 4 -2.361881 -0.21625333 FALSE #> 5 -2.125573  0.18747140 FALSE #> 6 -1.605805  0.39770940 FALSE #> 7 -2.319919 -0.27102366 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                      F1         F2  .sup #> Sepal.Length  0.9346494  0.3543710 FALSE #> Petal.Length  0.9852172 -0.1086968 FALSE #> Petal.Width   0.9668940 -0.2317965 FALSE #> Sepal.Width  -0.3160646  0.4483048  TRUE  ## Get correlations between variables and dimensions head(get_correlations(X)) #>                      F1         F2  .sup #> Sepal.Length  0.9346494  0.3543710 FALSE #> Petal.Length  0.9852172 -0.1086968 FALSE #> Petal.Width   0.9668940 -0.2317965 FALSE #> Sepal.Width  -0.3160646  0.4483048  TRUE  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.7791065 93.565365   93.56536 #> F2   0.1911235  6.434635  100.00000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/benthos.html","id":null,"dir":"Reference","previous_headings":"","what":"Benthos — benthos","title":"Benthos — benthos","text":"Abundances Marine Species Sea-Bed Samples","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/benthos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Benthos — benthos","text":"","code":"benthos"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/benthos.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Benthos — benthos","text":"data.frame 13 columns (sites) 92 rows (species).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/benthos.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Benthos — benthos","text":"http://www.carme-n.org/?sec=data7","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Biplot — biplot","title":"Biplot — biplot","text":"Biplot","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biplot — biplot","text":"","code":"# S4 method for CA biplot(   x,   axes = c(1, 2),   type = c(\"rows\", \"columns\", \"contributions\"),   active = TRUE,   sup = TRUE,   labels = \"columns\",   col.rows = \"#004488\",   col.columns = \"#BB5566\",   cex.rows = graphics::par(\"cex\"),   cex.columns = graphics::par(\"cex\"),   pch.rows = 16,   pch.columns = 17,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   ... )  # S4 method for PCA biplot(   x,   axes = c(1, 2),   type = c(\"form\", \"covariance\"),   active = TRUE,   sup = TRUE,   labels = \"variables\",   col.rows = \"#004488\",   col.columns = \"#BB5566\",   pch.rows = 16,   pch.columns = 17,   lty = \"solid\",   lwd = 2,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   ... )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Biplot — biplot","text":"x CA PCA object. axes length-two numeric vector giving dimensions plotted. type character string specifying biplot plotted (see ). must one \"rows\", \"columns\", \"contribution\" (CA), \"form\" \"covariance\" (PCA). unambiguous substring can given. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels character vector specifying whether \"rows\"/\"individuals\" /\"columns\"/\"variables\" names must drawn. unambiguous substring can given. col.rows, col.columns color specification. cex.rows, cex.columns numerical vector giving amount plotting characters symbols scaled relative default. pch.rows, pch.columns symbol specification. xlim length-two numeric vector giving x limits plot. default value, NULL, indicates range finite values plotted used. ylim length-two numeric vector giving y limits plot. default value, NULL, indicates range finite values plotted used. main character string giving main title plot. sub character string giving subtitle plot. ... parameters passed wordcloud::wordlayout(). lty, lwd specification line type width.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Biplot — biplot","text":"biplot() called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Biplot — biplot","text":"biplot simultaneous representation rows columns rectangular dataset. generalization scatterplot case mutlivariate data: allows visualize much information possible single graph (Greenacre 2010). Biplots drawbacks advantages: can quickly become difficult read display lot information . may preferable visualize results individuals variables separately.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"pca-biplots","dir":"Reference","previous_headings":"","what":"PCA Biplots","title":"Biplot — biplot","text":"form Form biplot (row-metric-preserving). form biplot favors representation individuals: distance individuals approximates Euclidean distance rows. form biplot length vector approximates quality representation variable. covariance Covariance biplot (column-metric-preserving). covariance biplot favors representation variables: length vector approximates standard deviation variable cosine angle formed two vectors approximates correlation two variables. covariance biplot distance individuals approximates Mahalanobis distance rows.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"ca-biplots","dir":"Reference","previous_headings":"","what":"CA Biplots","title":"Biplot — biplot","text":"rows Row principal biplot. columns Column principal biplot. contribution Contribution biplot","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Biplot — biplot","text":"Aitchison, J. Greenacre, M. (2002). Biplots Compositional Data. Journal Royal Statistical Society: Series C (Applied Statistics), 51(4): 375-92. doi:10.1111/1467-9876.00275 . Greenacre, M. J. Biplots Practice. Bilbao: Fundación BBVA, 2010.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Biplot — biplot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Biplot — biplot","text":"","code":"## Replicate examples from Greenacre 2007, p. 59-68 data(\"countries\")  ## Compute principal components analysis ## All rows and all columns obtain the same weight row_w <- rep(1 / nrow(countries), nrow(countries)) # 1/13 col_w <- rep(1 / ncol(countries), ncol(countries)) # 1/6 Y <- pca(countries, scale = FALSE, weight_row = row_w, weight_col = col_w)  ## Row-metric-preserving biplot (form biplot) biplot(Y, type = \"form\")   ## Column-metric-preserving biplot (covariance biplot) biplot(Y, type = \"covariance\")   ## Replicate examples from Greenacre 2007, p. 79-88 data(\"benthos\")  ## Compute correspondence analysis X <- ca(benthos)  ## Row principal CA biplot biplot(X, type = \"row\", labels = \"columns\")   ## Column principal CA biplot biplot(X, type = \"column\", labels = \"columns\")   ## Contribution CA biplot biplot(X, type = \"contrib\", labels = NULL)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial Bootstrap Analysis — bootstrap","title":"Partial Bootstrap Analysis — bootstrap","text":"Checks analysis partial bootstrap resampling.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial Bootstrap Analysis — bootstrap","text":"","code":"bootstrap(object, ...)  # S4 method for CA bootstrap(object, n = 30)  # S4 method for PCA bootstrap(object, n = 30)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial Bootstrap Analysis — bootstrap","text":"object CA PCA object. ... Currently used. n non-negative integer giving number bootstrap replications.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial Bootstrap Analysis — bootstrap","text":"#' Returns BootstrapCA BootstrapPCA object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial Bootstrap Analysis — bootstrap","text":"Greenacre, Michael J. Theory Applications Correspondence Analysis. London: Academic Press, 1984. Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial Bootstrap Analysis — bootstrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial Bootstrap Analysis — bootstrap","text":"","code":"## Bootstrap on CA ## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## Compute correspondence analysis X <- ca(colours)  ## Bootstrap (30 replicates) Y <- bootstrap(X, n = 30)  if (FALSE) { ## Get replicated coordinates get_replications(Y, margin = 1) get_replications(Y, margin = 2) }  ## Plot with ellipses viz_rows(Y) viz_tolerance(Y, margin = 1, level = c(0.68, 0.95))   viz_columns(Y) viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))   ## Plot with convex hulls viz_columns(Y) viz_hull(Y, margin = 2)   ## Bootstrap on PCA ## Compute principal components analysis data(\"iris\") X <- pca(iris) #> 1 qualitative variable was removed: Species.  ## Bootstrap (30 replicates) Y <- bootstrap(X, n = 30)  ## Plot with ellipses viz_variables(Y) viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":null,"dir":"Reference","previous_headings":"","what":"Correspondence Analysis — ca","title":"Correspondence Analysis — ca","text":"Computes simple correspondence analysis based singular value decomposition.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correspondence Analysis — ca","text":"","code":"ca(object, ...)  # S4 method for data.frame ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)  # S4 method for matrix ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correspondence Analysis — ca","text":"object \\(m \\times p\\) numeric matrix data.frame. ... Currently used. rank integer value specifying maximal number components kept results. NULL (default), \\(min(m, p) - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows. sup_col numeric logical vector specifying indices supplementary columns.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correspondence Analysis — ca","text":"CA object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correspondence Analysis — ca","text":"Greenacre, M. J. Theory Applications Correspondence Analysis. London: Academic Press, 1984. Greenacre, M. J. Correspondence Analysis Practice. Seconde edition. Interdisciplinary Statistics Series. Boca Raton: Chapman & Hall/CRC, 2007. Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correspondence Analysis — ca","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correspondence Analysis — ca","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## The chi square of independence between the two variables stats::chisq.test(colours) #>  #> \tPearson's Chi-squared test #>  #> data:  colours #> X-squared = 138.29, df = 9, p-value < 2.2e-16 #>   ## Compute correspondence analysis X <- ca(colours)  ## Plot rows viz_rows(X, labels = TRUE)   ## Plot columns viz_columns(X, labels = TRUE)   ## Get row coordinates head(get_coordinates(X, margin = 1)) #>                  F1          F2           F3  .sup #> marron   -0.4921577 -0.08832151  0.021611305 FALSE #> noisette -0.2125969  0.16739109 -0.100518284 FALSE #> vert      0.1617534  0.33903957  0.087597437 FALSE #> bleu      0.5474139 -0.08295428 -0.004709408 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                 F1          F2          F3  .sup #> brun    -0.5045624 -0.21482046  0.05550909 FALSE #> chatain -0.1482527  0.03266635 -0.04880414 FALSE #> roux    -0.1295233  0.31964240  0.08315117 FALSE #> blond    0.8353478 -0.06957934  0.01621471 FALSE  ## Get row distances to centroid head(get_distances(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.25048691 0.08332116 0.14878530 0.30656555   ## Get row inertias head(get_inertia(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.09308635 0.01308930 0.01608490 0.11133715   ## Get row contributions head(get_contributions(X, margin = 1)) #>                 F1       F2         F3 #> marron   43.115744 13.04249  6.6795992 #> noisette  3.400961 19.80398 61.0855951 #> vert      1.354851 55.90952 31.9248234 #> bleu     52.128445 11.24401  0.3099822  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1 0.208772652 89.372732   89.37273 #> F2 0.022226615  9.514911   98.88764 #> F3 0.002598439  1.112356  100.00000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/colours.html","id":null,"dir":"Reference","previous_headings":"","what":"Colours — colours","title":"Colours — colours","text":"Contingency table eye hair colours different individuals.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/colours.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Colours — colours","text":"","code":"colours"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/colours.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Colours — colours","text":"data.frame 4 columns (hair colours) 4 rows (eye colours).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/colours.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Colours — colours","text":"Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006, p. 170-172","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/countries.html","id":null,"dir":"Reference","previous_headings":"","what":"Countries — countries","title":"Countries — countries","text":"Student ratings 13 countries six attributes.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/countries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Countries — countries","text":"","code":"countries"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/countries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Countries — countries","text":"data.frame 6 columns (attributes) 13 rows (countries).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/countries.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Countries — countries","text":"Greenacre, M. J. Biplots Practice. Bilbao: Fundación BBVA, 2010.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimensio-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in dimensio — dimensio-deprecated","title":"Deprecated Functions in dimensio — dimensio-deprecated","text":"functions still work removed (defunct) next version.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimensio-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions in dimensio — dimensio-deprecated","text":"","code":"stat_hull(   mapping = NULL,   data = NULL,   geom = \"polygon\",   position = \"identity\",   na.rm = FALSE,   show.legend = NA,   inherit.aes = TRUE,   ... )  plot_rows(object, ...)  # S4 method for MultivariateAnalysis plot_rows(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_columns(object, ...)  # S4 method for MultivariateAnalysis plot_columns(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  # S4 method for BootstrapPCA plot_columns(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_individuals(object, ...)  # S4 method for PCA plot_individuals(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_variables(object, ...)  # S4 method for PCA plot_variables(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   linetype = NULL,   size = NULL,   group = NULL )  plot_variance(object, ...)  # S4 method for MultivariateAnalysis plot_variance(   object,   select = NULL,   variance = TRUE,   cumulative = TRUE,   fill = \"grey30\",   border = \"grey10\",   colour = \"red\" )  plot_contributions(object, ...)  # S4 method for MultivariateAnalysis plot_contributions(   object,   margin = 2,   axes = 1,   sort = TRUE,   decreasing = TRUE,   limit = 10,   fill = \"grey30\",   border = \"grey10\" )  plot_cos2(object, ...)  # S4 method for MultivariateAnalysis plot_cos2(   object,   margin = 2,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   sort = TRUE,   decreasing = TRUE,   limit = 10,   fill = \"grey30\",   border = \"grey10\" )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimensio-package.html","id":null,"dir":"Reference","previous_headings":"","what":"dimensio: Multivariate Data Analysis — dimensio-package","title":"dimensio: Multivariate Data Analysis — dimensio-package","text":"Simple Principal Components Analysis (PCA) Correspondence Analysis (CA) based Singular Value Decomposition (SVD). package provides S4 classes methods compute, extract, summarize visualize results multivariate data analysis. also includes methods partial bootstrap validation described Greenacre (1984) <isbn:978-0-12-299050-2> Lebart et al. (2006) <isbn:978-2-10-049616-7>.","code":""},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimensio-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"dimensio: Multivariate Data Analysis — dimensio-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 F-33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimnames.html","id":null,"dir":"Reference","previous_headings":"","what":"Dimnames of an Object — dimnames","title":"Dimnames of an Object — dimnames","text":"Retrieve set dimnames object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimnames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dimnames of an Object — dimnames","text":"","code":"# S4 method for MultivariateAnalysis dim(x)  # S4 method for MultivariateAnalysis rownames(x, do.NULL = TRUE, prefix = \"row\")  # S4 method for MultivariateAnalysis colnames(x, do.NULL = TRUE, prefix = \"col\")  # S4 method for MultivariateAnalysis dimnames(x)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimnames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dimnames of an Object — dimnames","text":"x object retrieve row column names (CA PCA object). .NULL logical scalar. FALSE names NULL, names created. prefix character string specifying prefix created names.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimnames.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dimnames of an Object — dimnames","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Contributions — get_contributions","title":"Get Contributions — get_contributions","text":"Get Contributions","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Contributions — get_contributions","text":"","code":"get_contributions(x, ...)  get_correlations(x, ...)  get_cos2(x, ...)  # S4 method for MultivariateAnalysis get_contributions(x, margin = 1)  # S4 method for PCA get_correlations(x, sup_name = \".sup\")  # S4 method for MultivariateAnalysis get_cos2(x, margin = 1, sup_name = \".sup\")"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Contributions — get_contributions","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. sup_name character string specifying name column create supplementary points attribution (see ).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Contributions — get_contributions","text":"get_contributions() returns data.frame contributions definition principal dimensions. get_correlations() returns data.frame correlations variables dimensions (PCA). extra column (named sup_name) added specifying whether observation supplementary point . get_cos2() returns data.frame \\(cos^2\\) values (.e. quality representation points factor map). extra column (named sup_name) added specifying whether observation supplementary point .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Contributions — get_contributions","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Coordinates — get_coordinates","title":"Get Coordinates — get_coordinates","text":"Get Coordinates","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Coordinates — get_coordinates","text":"","code":"get_coordinates(x, ...)  get_replications(x, ...)  # S4 method for MultivariateAnalysis get_coordinates(x, margin = 1, principal = TRUE, sup_name = \".sup\")  # S4 method for MultivariateBootstrap get_replications(x, margin = 1)  # S4 method for BootstrapPCA get_replications(x)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Coordinates — get_coordinates","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. principal logical scalar: principal coordinates returned? FALSE, standard coordinates returned. sup_name character string specifying name column create supplementary points attribution (see ).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Coordinates — get_coordinates","text":"get_coordinates() returns data.frame coordinates. extra column (named sup_name) added specifying whether observation supplementary point . get_replications() returns array coordinates.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Coordinates — get_coordinates","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Coordinates — get_coordinates","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 5:10) #> 1 qualitative variable was removed: Species.  ## Get row principal coordinates head(get_coordinates(X, margin = 1, principal = TRUE)) #>           F1         F2          F3  .sup #> 1  -2.385846  0.5084067 -0.12383101 FALSE #> 2  -2.215654 -0.6515155 -0.23313750 FALSE #> 3  -2.496566 -0.3169280  0.04974232 FALSE #> 4  -2.433607 -0.5744432  0.09690301 FALSE #> 11 -2.279483  1.0742447 -0.26629760 FALSE #> 12 -2.450871  0.1591839  0.09988982 FALSE  ## Get row standard coordinates head(get_coordinates(X, margin = 1, principal = FALSE)) #>          F1         F2         F3  .sup #> 1 -1.403075  0.5268995 -0.3142569 FALSE #> 2 -1.302987 -0.6752138 -0.5916536 FALSE #> 3 -1.468187 -0.3284560  0.1262355 FALSE #> 4 -1.431162 -0.5953380  0.2459193 FALSE #> 5 -1.340525  1.1133193 -0.6758069 FALSE #> 6 -1.441315  0.1649741  0.2534992 FALSE  ## Tidy principal coordinates head(tidy(X, margin = 1)) #>   label component supplementary coordinate contribution        cos2 #> 1     1        F1         FALSE -2.3858463   1.36709655 0.954033073 #> 2     1        F2         FALSE  0.5084067   0.19279380 0.043321256 #> 3     1        F3         FALSE -0.1238310   0.06858151 0.002570025 #> 4    10        F1          TRUE -2.3173283           NA 0.953213276 #> 5    10        F2          TRUE -0.4448514           NA 0.035127301 #> 6    10        F3          TRUE -0.2525218           NA 0.011319124 head(tidy(X, margin = 2)) #>          label component supplementary coordinate contribution         cos2 #> 1 Petal.Length        F1         FALSE 0.99121597  33.97921607 0.9825091061 #> 2 Petal.Length        F2         FALSE 0.01655680   0.02944324 0.0002741275 #> 3 Petal.Length        F3         FALSE 0.05477927   1.93260521 0.0030007681 #> 4  Petal.Width        F1         FALSE 0.96350955  32.10619376 0.9283506618 #> 5  Petal.Width        F2         FALSE 0.05756108   0.35586957 0.0033132775 #> 6  Petal.Width        F3         FALSE 0.24948796  40.08758618 0.0622442430  head(augment(X, margin = 1, axes = c(1, 2))) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.385846  0.5084067     1         FALSE 0.006944444 5.950740     4.132458 #> 2 -2.215654 -0.6515155     2         FALSE 0.006944444 5.333593     3.703884 #> 3 -2.496566 -0.3169280     3         FALSE 0.006944444 6.333284     4.398114 #> 4 -2.433607 -0.5744432     4         FALSE 0.006944444 6.252426     4.341962 #> 5 -2.279483  1.0742447    11         FALSE          NA 6.350046     4.409754 #> 6 -2.450871  0.1591839    12         FALSE          NA 6.032107     4.188963 #>        cos2 #> 1 0.9973543 #> 2 0.9880287 #> 3 0.9995093 #> 4 0.9977072 #> 5 0.9889258 #> 6 0.9951651 head(augment(X, margin = 2, axes = c(1, 2))) #>           F1         F2        label supplementary mass       sum contribution #> 1  0.8875399 0.36087785 Sepal.Length         FALSE    1 0.9179599     91.79599 #> 2 -0.4392190 0.89286998  Sepal.Width         FALSE    1 0.9901302     99.01302 #> 3  0.9912160 0.01655680 Petal.Length         FALSE    1 0.9827832     98.27832 #> 4  0.9635096 0.05756108  Petal.Width         FALSE    1 0.9316639     93.16639 #>        cos2 #> 1 0.9179599 #> 2 0.9901302 #> 3 0.9827832 #> 4 0.9316639"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Original Data — get_data","title":"Get Original Data — get_data","text":"Get Original Data","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Original Data — get_data","text":"","code":"get_data(x, ...)  # S4 method for MultivariateAnalysis get_data(x)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Original Data — get_data","text":"x object get element(s) (CA PCA object). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Original Data — get_data","text":"Returns data.frame original data.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Original Data — get_data","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Distances — get_distances","title":"Get Distances — get_distances","text":"Get Distances","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Distances — get_distances","text":"","code":"get_distances(x, ...)  # S4 method for MultivariateAnalysis get_distances(x, margin = 1)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Distances — get_distances","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Distances — get_distances","text":"get_distances() returns numeric vector squared distance centroide.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Distances — get_distances","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Eigenvalues — get_eigenvalues","title":"Get Eigenvalues — get_eigenvalues","text":"Get Eigenvalues","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Eigenvalues — get_eigenvalues","text":"","code":"get_eigenvalues(x)  get_inertia(x, ...)  get_variance(x, ...)  # S4 method for MultivariateAnalysis get_eigenvalues(x)  # S4 method for MultivariateAnalysis get_inertia(x, margin = 1)  # S4 method for MultivariateAnalysis get_variance(x, digits = 2)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Eigenvalues — get_eigenvalues","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. digits integer indicating number decimal places used.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Eigenvalues — get_eigenvalues","text":"get_eigenvalues() returns data.frame following columns: eigenvalues, variance (percentage variance) cumulative (cumulative percentage variance). get_variance() returns numeric vector giving percentage explained variance dimension. get_inertia() returns numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Eigenvalues — get_eigenvalues","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Loadings — loadings","title":"Extract Loadings — loadings","text":"Extract loadingsin principal components analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Loadings — loadings","text":"","code":"# S4 method for PCA loadings(x)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Loadings — loadings","text":"x PCA object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Loadings — loadings","text":"Returns variable loadings (.e. coefficients linear combination original variables).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Extract Loadings — loadings","text":"loadings() implemented consistency [stats][stats::loadings].","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Loadings — loadings","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis — pca","title":"Principal Components Analysis — pca","text":"Computes principal components analysis based singular value decomposition.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis — pca","text":"","code":"pca(object, ...)  # S4 method for data.frame pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for matrix pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis — pca","text":"object \\(m \\times p\\) numeric matrix data.frame. ... Currently used. center logical scalar: variables shifted zero centered? scale logical scalar: variables scaled unit variance? rank integer value specifying maximal number components kept results. NULL (default), \\(p - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows (individuals). sup_col numeric logical vector specifying indices supplementary columns (variables). weight_row numeric vector specifying active row (individual) weights. NULL (default), uniform weights used. Row weights internally normalized sum 1 weight_col numeric vector specifying active column (variable) weights. NULL (default), uniform weights (1) used.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis — pca","text":"PCA object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Principal Components Analysis — pca","text":"Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis — pca","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis — pca","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2) #> 1 qualitative variable was removed: Species.  ## Get row coordinates head(get_coordinates(X, margin = 1)) #>          F1          F2  .sup #> 1 -2.037750  0.28449650 FALSE #> 2 -2.173723  0.08693737 FALSE #> 3 -2.343172 -0.09618534 FALSE #> 4 -2.344208 -0.22383774 FALSE #> 5 -2.105737  0.18571694 FALSE #> 6 -1.582347  0.40133551 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                      F1         F2  .sup #> Sepal.Length  0.9310079  0.3638965 FALSE #> Petal.Length  0.9843567 -0.1142004 FALSE #> Petal.Width   0.9659776 -0.2343497 FALSE #> Sepal.Width  -0.3174565  0.4886782  TRUE  ## Get correlations between variables and dimensions head(get_correlations(X)) #>                      F1         F2  .sup #> Sepal.Length  0.9310079  0.3638965 FALSE #> Petal.Length  0.9843567 -0.1142004 FALSE #> Petal.Width   0.9659776 -0.2343497 FALSE #> Sepal.Width  -0.3174565  0.4886782  TRUE  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.7688465 93.251372   93.25137 #> F2   0.2003822  6.748628  100.00000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict New Coordinates — predict","title":"Predict New Coordinates — predict","text":"Predict projection new individuals/rows variables/columns.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict New Coordinates — predict","text":"","code":"# S4 method for CA predict(object, newdata, margin = 1)  # S4 method for PCA predict(object, newdata, margin = 1)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict New Coordinates — predict","text":"object CA PCA object. newdata object supplementary points coercible matrix compute principal coordinates. margin length-one numeric vector giving subscript data predicted: 1 indicates individuals/rows (default), 2 indicates variables/columns.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict New Coordinates — predict","text":"data.frame coordinates.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict New Coordinates — predict","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict New Coordinates — predict","text":"","code":"## Create a matrix A <- matrix(data = sample(1:10, 100, TRUE), nrow = 10, ncol = 10)  ## Compute correspondence analysis X <- ca(A, sup_row = 8:10, sup_col = 7:10)  ## Predict new row coordinates Y <- matrix(data = sample(1:10, 120, TRUE), nrow = 20, ncol = 6) predict(X, Y, margin = 1) #>             F1          F2          F3          F4          F5 #> 1  -0.11393136  0.12196021 -0.03933027  0.07547897  0.22868955 #> 2  -0.36245566  0.34426865 -0.47883474  0.21197807 -0.16705579 #> 3   0.10458280  0.22830858  0.53864849  0.01975142 -0.30258976 #> 4  -0.29542894  0.14813156  0.13537729  0.17444654 -0.28157716 #> 5  -0.02465857  0.05552166  0.17514484  0.15150058 -0.09443768 #> 6   0.10813085  0.21997827 -0.19071388  0.19689191 -0.54721259 #> 7  -0.37242363 -0.01568988  0.18957832  0.04289928 -0.19863331 #> 8   0.17257300  0.17911758  0.08824491 -0.06422399  0.44277505 #> 9   0.07707648  0.19986807  0.25371803  0.18164763 -0.22047288 #> 10 -0.07236992  0.18254891  0.04612638  0.48076918  0.13052350 #> 11  0.40533664  0.06978921 -0.18065696  0.46788388 -0.04925436 #> 12 -0.16644791  0.17671218 -0.01710058 -0.08502750 -0.14945800 #> 13 -0.09970503  0.12432823 -0.13474191  0.57986567  0.59065788 #> 14  0.12139432 -0.21341644  0.15966991  0.10509173  0.09162720 #> 15 -0.19982542 -0.06666130  0.10155244  0.42434594 -0.20052918 #> 16  0.14729423  0.21361720 -0.26987112  0.15856722 -0.42553311 #> 17  0.02571276  0.15473673  0.10631792  0.08367243 -0.36746158 #> 18  0.47560029  0.06889942  0.11271582  0.26929808  0.03044542 #> 19 -0.02630321  0.12540419 -0.37097389  0.29947778  0.33343898 #> 20  0.15506270  0.24345068  0.32268311 -0.18034512 -0.06256898  ## Predict new column coordinates Z <- matrix(data = sample(1:10, 140, TRUE), nrow = 7, ncol = 20) predict(X, Z, margin = 2) #>              F1          F2          F3          F4           F5 #> 1  -0.014348453  0.02428119 -0.23553827 -0.04363336  0.095996772 #> 2   0.253798662  0.07319638  0.41986009  0.24987843 -0.005412720 #> 3   0.127891431 -0.07966325  0.08669107 -0.02128859 -0.170614788 #> 4   0.117044566  0.22911437  0.23148916  0.04901558 -0.258025402 #> 5  -0.068236532 -0.33260007  0.18317602 -0.22303068 -0.035566346 #> 6  -0.002347086 -0.08444443 -0.12251044 -0.09982256 -0.157513109 #> 7  -0.198671358  0.17230814  0.25896714 -0.04459512 -0.080602364 #> 8  -0.385907223 -0.18988897  0.29818477 -0.01087143  0.063660402 #> 9  -0.074609791  0.04924708 -0.47131970  0.07851356 -0.092664017 #> 10 -0.154511317  0.22949038  0.43205684 -0.21288387 -0.021507839 #> 11 -0.304891966  0.03488418  0.46609342  0.01606043  0.246270360 #> 12 -0.127321998  0.14709106  0.08247683 -0.25801248  0.174759416 #> 13 -0.246324025  0.13786864  0.02890378  0.11672619  0.166996621 #> 14 -0.223390576  0.13350105  0.13369598 -0.09144535  0.216573789 #> 15 -0.131721425 -0.12198161  0.41155539 -0.24856352 -0.236235957 #> 16  0.093521292 -0.12964924  0.34384056  0.02304465  0.108082767 #> 17 -0.025465795 -0.02249537  0.05746759 -0.29993442  0.005918782 #> 18 -0.138221744  0.35583543 -0.13878305  0.18888950  0.028078939 #> 19 -0.254076079 -0.03922454 -0.29123610  0.31611310 -0.214836059 #> 20 -0.167505264  0.17521753  0.05120108 -0.38165447  0.015001565"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Scree Plot — screeplot","title":"Scree Plot — screeplot","text":"Plot eigenvalues (scree plot) variances histogram.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scree Plot — screeplot","text":"","code":"# S4 method for MultivariateAnalysis screeplot(   x,   eigenvalues = FALSE,   cumulative = FALSE,   labels = TRUE,   limit = 10,   col = \"grey90\",   border = \"grey10\",   col.cumulative = \"red\",   lty.cumulative = \"solid\",   lwd.cumulative = 2,   ... )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scree Plot — screeplot","text":"x CA PCA object. eigenvalues logical scalar: eigenvalues plotted instead variance/inertia? cumulative logical scalar: cumulative percentages variance plotted? labels logical scalar: text labels drawn top bars? limit integer specifying number top elements displayed. col, border character string specifying bars infilling border colors. col.cumulative specification line color. lty.cumulative specification line type. lwd.cumulative specification line width. ... Extra parameters passed graphics::barplot().","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scree Plot — screeplot","text":"screeplot() called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scree Plot — screeplot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scree Plot — screeplot","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Screeplot screeplot(X)  screeplot(X, cumulative = TRUE)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Parts of an Object — subset","title":"Extract Parts of an Object — subset","text":"Operators acting objects extract parts.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Parts of an Object — subset","text":"","code":"# S4 method for CA,ANY,missing [[(x, i)  # S4 method for PCA,ANY,missing [[(x, i)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Parts of an Object — subset","text":"x object extract element(s) replace element(s). character string specifying elements extract. unambiguous substring can given (see details).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Parts of an Object — subset","text":"list.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Parts of an Object — subset","text":"\"data\", returns list following elements: data numeric matrix raw data. mean numeric vector giving variables means (PCA). sd numeric vector giving variables standard deviations (PCA). \"rows\", returns list following elements: coord numeric matrix rows/individuals coordinates. cos2 numeric matrix rows/individuals squared cosine. masses numeric vector giving rows masses/individual weights. sup logical vector specifying whether point supplementary observation . \"columns\", returns list following elements: coord numeric matrix columns/variables coordinates. cor numeric matrix correlation variables dimensions (PCA). cos2 numeric matrix columns/variables squared cosine. masses numeric vector giving columns masses/variable weights. sup logical vector specifying whether point supplementary observation . \"eigenvalues\", returns numeric vector eigenvalues.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Parts of an Object — subset","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Parts of an Object — subset","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 8:10, sup_col = 1) #> 1 qualitative variable was removed: Species.  ## Get results for the individuals X[[\"rows\"]] #> $coordinates #>             F1           F2 #> 1   -2.1799607  0.107860619 #> 2   -1.6996139 -0.926588874 #> 3   -1.9288393 -0.527485410 #> 4   -1.7585967 -0.705022642 #> 5   -2.2760301  0.314750517 #> 6   -2.2841520  1.069266219 #> 7   -1.9994783 -0.054120776 #> 11  -2.3350128  0.536316749 #> 12  -2.0097181 -0.069676614 #> 13  -1.7840271 -0.971497378 #> 14  -1.8952870 -1.015526377 #> 15  -2.7344808  1.112957446 #> 16  -2.8386721  2.074363046 #> 17  -2.4324986  1.010560888 #> 18  -2.0955476  0.152769123 #> 19  -2.2724958  0.817467817 #> 20  -2.3466691  0.788115151 #> 21  -1.9726315 -0.055000281 #> 22  -2.1661866  0.626133757 #> 23  -2.4243767  0.256045186 #> 24  -1.6233228 -0.127164668 #> 25  -1.8984582 -0.025647615 #> 26  -1.6254407 -0.897236208 #> 27  -1.8408919  0.020140394 #> 28  -2.1428741  0.122536952 #> 29  -2.0838914 -0.099029280 #> 30  -1.8175794 -0.483456411 #> 31  -1.7215100 -0.690346310 #> 32  -1.8779785  0.005464061 #> 33  -2.8037034  1.318967839 #> 34  -2.8524463  1.556089908 #> 35  -1.7585967 -0.705022642 #> 36  -1.9659259 -0.542161743 #> 37  -2.2170474  0.093184286 #> 38  -2.3604432  0.269842013 #> 39  -1.7367006 -0.941265207 #> 40  -2.0468047 -0.084352947 #> 41  -2.1326343  0.138092790 #> 42  -0.9798020 -2.344585993 #> 43  -1.9288393 -0.527485410 #> 44  -1.7681350  0.316847300 #> 45  -2.1139094  0.891728987 #> 46  -1.6152008 -0.881680370 #> 47  -2.3939955  0.757882980 #> 48  -1.8917527 -0.512809077 #> 49  -2.3350128  0.536316749 #> 50  -1.9878220 -0.305919178 #> 51   0.3450638  0.510411957 #> 52   0.3553036  0.525967795 #> 53   0.5997195  0.377783228 #> 54   0.8656684 -1.499239964 #> 55   0.7766677 -0.286915466 #> 56   0.5707548 -0.391408807 #> 57   0.4178206  0.807118863 #> 58   0.2567533 -1.529809908 #> 59   0.5117721 -0.169842576 #> 60   0.5287175 -0.641448199 #> 61   0.7152040 -2.328016836 #> 62   0.4361824  0.068158999 #> 63   0.7084985 -1.840855375 #> 64   0.6332719 -0.110257739 #> 65   0.1409057 -0.316605905 #> 66   0.3298732  0.259493060 #> 67   0.5474423  0.112187998 #> 68   0.2652383 -0.791729549 #> 69   1.3159972 -1.542931190 #> 70   0.4676168 -1.189953508 #> 71   0.7198029  0.704722305 #> 72   0.3853216 -0.464790472 #> 73   1.1761357 -0.863556163 #> 74   0.5605150 -0.406964645 #> 75   0.4005122 -0.213871574 #> 76   0.4259426  0.052603161 #> 77   0.7664279 -0.302471304 #> 78   0.9017017  0.275386670 #> 79   0.6435117 -0.094701901 #> 80   0.1387878 -1.086677445 #> 81   0.5265996 -1.411519740 #> 82   0.4050998 -1.471104576 #> 83   0.3598912 -0.731265207 #> 84   1.1425834 -0.375515196 #> 85   0.5474423  0.112187998 #> 86   0.2475780  0.984656096 #> 87   0.5255462  0.348430562 #> 88   1.0140150 -1.440534633 #> 89   0.2302695 -0.036334342 #> 90   0.6735297 -1.085460167 #> 91   0.6413938 -0.864773441 #> 92   0.5001159  0.081955827 #> 93   0.4930472 -0.923478773 #> 94   0.3528226 -1.736699807 #> 95   0.5555643 -0.642327704 #> 96   0.1829431 -0.066566513 #> 97   0.3634255 -0.228547907 #> 98   0.4005122 -0.213871574 #> 99   0.1338371 -1.322040504 #> 100  0.4224083 -0.450114139 #> 101  1.6596649  1.402087727 #> 102  1.3958227 -0.240789684 #> 103  1.5731339  0.587107682 #> 104  1.3047041  0.201463273 #> 105  1.6204604  0.617339853 #> 106  1.8327404  0.689842013 #> 107  1.1966153 -0.832444487 #> 108  1.5643105  0.304197604 #> 109  1.7631548 -0.596743655 #> 110  1.4085435  2.037433755 #> 111  0.9998890  0.838568312 #> 112  1.4699960 -0.211437019 #> 113  1.4247874  0.528402351 #> 114  1.6352879 -0.624337311 #> 115  1.7218189  0.190642734 #> 116  1.3273016  1.002646490 #> 117  1.1715481  0.393676839 #> 118  1.1856853  2.404546038 #> 119  2.4971040 -0.003871575 #> 120  1.5014304 -1.469549526 #> 121  1.4756482  1.061351821 #> 122  1.3099931 -0.018343948 #> 123  1.9775527  0.245830045 #> 124  1.2372363 -0.315050854 #> 125  1.2107526  1.178424712 #> 126  1.1648425  0.880838301 #> 127  1.1040803 -0.122837289 #> 128  0.9490282  0.305618841 #> 129  1.6540127  0.129298887 #> 130  1.1139818  0.347888830 #> 131  1.6706197  0.112863543 #> 132  0.9055992  2.270700031 #> 133  1.7384258  0.174207390 #> 134  0.9621009 -0.213533802 #> 135  1.2552597 -0.598840438 #> 136  1.8161334  0.706277356 #> 137  1.3308359  1.505363789 #> 138  1.0754787  0.600566737 #> 139  0.9119416  0.290942508 #> 140  1.2916314  0.720615916 #> 141  1.6190440  0.884694094 #> 142  1.3491977  0.766403925 #> 143  1.3958227 -0.240789684 #> 144  1.5498214  1.090704487 #> 145  1.5484050  1.358058728 #> 146  1.4823537  0.574190360 #> 147  1.5508748 -0.669245814 #> 148  1.2291144  0.439464848 #> 149  1.1722495  1.431102620 #> 150  1.0232015  0.334971507 #> 8   -2.0468047 -0.084352947 #> 9   -1.6035446 -1.133478772 #> 10  -1.8430098 -0.749931146 #>  #> $contributions #>              F1           F2 #> 1   1.453343684 0.0106993487 #> 2   0.883428800 0.7895973615 #> 3   1.137792525 0.2558894786 #> 4   0.945809064 0.4571278240 #> 5   1.584261926 0.0911096611 #> 6   1.595588876 1.0514853344 #> 7   1.222656219 0.0026937641 #> 11  1.667437334 0.2645295822 #> 12  1.235211341 0.0044648369 #> 13  0.973360803 0.8679901399 #> 14  1.098552806 0.9484489111 #> 15  2.286760836 1.1391702764 #> 16  2.464344658 3.9573190026 #> 17  1.809573309 0.9391965689 #> 18  1.342969212 0.0214636064 #> 19  1.579345535 0.6145721192 #> 20  1.684126390 0.5712298316 #> 21  1.190043653 0.0027820270 #> 22  1.435035728 0.3605502185 #> 23  1.797509409 0.0602927030 #> 24  0.805899240 0.0148718312 #> 25  1.102232091 0.0006049584 #> 26  0.808003463 0.7403636858 #> 27  1.036400344 0.0003730503 #> 28  1.404314222 0.0138091094 #> 29  1.328070529 0.0090190079 #> 30  1.010317225 0.2149543180 #> 31  0.906337800 0.4382940161 #> 32  1.078579659 0.0000274576 #> 33  2.404003572 1.5999261560 #> 34  2.488318246 2.2269009410 #> 35  0.945809064 0.4571278240 #> 36  1.181966834 0.2703268996 #> 37  1.503214416 0.0079857717 #> 38  1.703954895 0.0669654302 #> 39  0.922403375 0.8148084750 #> 40  1.281220300 0.0065438305 #> 41  1.390925123 0.0175377362 #> 42  0.293594279 5.0554970160 #> 43  1.137792525 0.2558894786 #> 44  0.956096741 0.0923276004 #> 45  1.366607256 0.7313028984 #> 46  0.797855133 0.7149141115 #> 47  1.752740637 0.5282455348 #> 48  1.094459487 0.2418482414 #> 49  1.667437334 0.2645295822 #> 50  1.208442464 0.0860686388 #> 51  0.036414113 0.2395924925 #> 52  0.038607369 0.2544191706 #> 53  0.109993587 0.1312553072 #> 54  0.229178638 2.0671587361 #> 55  0.184476671 0.0757075942 #> 56  0.099625421 0.1408940695 #> 57  0.053388873 0.5991099343 #> 58  0.020160574 2.1523181354 #> 59  0.080098468 0.0265292309 #> 60  0.085490582 0.3784031185 #> 61  0.156433881 4.9842952461 #> 62  0.058184494 0.0042724593 #> 63  0.153514287 3.1165268007 #> 64  0.122645427 0.0111802030 #> 65  0.006071949 0.0921869714 #> 66  0.033278603 0.0619274246 #> 67  0.091653220 0.0115750885 #> 68  0.021515104 0.5764813136 #> 69  0.529640167 2.1893976274 #> 70  0.066873073 1.3022414468 #> 71  0.158452140 0.4567384375 #> 72  0.045406471 0.1986762522 #> 73  0.423044289 0.6858240370 #> 74  0.096082758 0.1523157768 #> 75  0.049057166 0.0420666239 #> 76  0.055484680 0.0025448091 #> 77  0.179644343 0.0841394918 #> 78  0.248654774 0.0697456870 #> 79  0.126643778 0.0082480050 #> 80  0.005890792 1.0860075196 #> 81  0.084807053 1.8323373666 #> 82  0.050187444 1.9903003497 #> 83  0.039610792 0.4917918728 #> 84  0.399251696 0.1296840484 #> 85  0.091653220 0.0115750885 #> 86  0.018745415 0.8916628530 #> 87  0.084468126 0.1116513580 #> 88  0.314455886 1.9084418605 #> 89  0.016216010 0.0012141326 #> 90  0.138734505 1.0835758275 #> 91  0.125811545 0.6877588893 #> 92  0.076491334 0.0061771934 #> 93  0.074344362 0.7843056805 #> 94  0.038070081 2.7738376469 #> 95  0.094392957 0.3794415052 #> 96  0.010235356 0.0040751457 #> 97  0.040392607 0.0480381224 #> 98  0.049057166 0.0420666239 #> 99  0.005478023 1.6073892100 #> 100 0.054567719 0.1863274482 #> 101 0.842387320 1.8079312085 #> 102 0.595842510 0.0533221045 #> 103 0.756837054 0.3170056865 #> 104 0.520589025 0.0373270182 #> 105 0.803059702 0.3504936509 #> 106 1.027242319 0.4376539041 #> 107 0.437905211 0.6372973156 #> 108 0.748370960 0.0851026540 #> 109 0.950718311 0.3274968644 #> 110 0.606752411 3.8176711884 #> 111 0.305755696 0.6467082790 #> 112 0.660850565 0.0411143642 #> 113 0.620827724 0.2567798899 #> 114 0.817823172 0.3584842707 #> 115 0.906663024 0.0334250480 #> 116 0.538778450 0.9245431850 #> 117 0.419750477 0.1425316314 #> 118 0.429941982 5.3173802664 #> 119 1.906971687 0.0000137850 #> 120 0.689416059 1.9860948268 #> 121 0.665942333 1.0359773621 #> 122 0.524818373 0.0003094690 #> 123 1.195988781 0.0555778122 #> 124 0.468140574 0.0912836189 #> 125 0.448313474 1.2771303006 #> 126 0.414959229 0.7135491729 #> 127 0.372796904 0.0138768844 #> 128 0.275441407 0.0858997255 #> 129 0.836659379 0.0153752114 #> 130 0.379513426 0.1113044416 #> 131 0.853544550 0.0117149082 #> 132 0.250808937 4.7418854471 #> 133 0.924236944 0.0279103111 #> 134 0.283081973 0.0419338551 #> 135 0.481879173 0.3298023644 #> 136 1.008710388 0.4587563529 #> 137 0.541651566 2.0840803395 #> 138 0.353732210 0.3317065721 #> 139 0.254334345 0.0778477176 #> 140 0.510209050 0.4775724073 #> 141 0.801656426 0.7198098450 #> 142 0.556701191 0.5401905310 #> 143 0.595842510 0.0533221045 #> 144 0.734571902 1.0940715530 #> 145 0.733229824 1.6961670731 #> 146 0.672008322 0.3032098599 #> 147 0.735570728 0.4119104916 #> 148 0.462014453 0.1776150699 #> 149 0.420253277 1.8835323329 #> 150 0.320179346 0.1031922926 #>  #> $cos2 #>              F1           F2 #> 1   0.997537883 2.442069e-03 #> 2   0.770275213 2.289389e-01 #> 3   0.929285621 6.949892e-02 #> 4   0.861533164 1.384668e-01 #> 5   0.981140144 1.876324e-02 #> 6   0.820250332 1.797497e-01 #> 7   0.997006440 7.304536e-04 #> 11  0.948921305 5.006041e-02 #> 12  0.997254638 1.198698e-03 #> 13  0.771015900 2.286355e-01 #> 14  0.775718588 2.227084e-01 #> 15  0.857879117 1.421129e-01 #> 16  0.651874030 3.480990e-01 #> 17  0.849498051 1.466163e-01 #> 18  0.993184069 5.278443e-03 #> 19  0.884472859 1.144510e-01 #> 20  0.898639841 1.013588e-01 #> 21  0.995527356 7.739112e-04 #> 22  0.920782450 7.693067e-02 #> 23  0.985634960 1.099383e-02 #> 24  0.983278582 6.033923e-03 #> 25  0.988580295 1.804280e-04 #> 26  0.766287189 2.334871e-01 #> 27  0.996619952 1.192913e-04 #> 28  0.996181238 3.257461e-03 #> 29  0.997744694 2.253183e-03 #> 30  0.933168940 6.602192e-02 #> 31  0.861052131 1.384664e-01 #> 32  0.993951874 8.414246e-06 #> 33  0.814686002 1.802995e-01 #> 34  0.769931500 2.291324e-01 #> 35  0.861533164 1.384668e-01 #> 36  0.926583440 7.047060e-02 #> 37  0.998037699 1.763122e-03 #> 38  0.984800233 1.287005e-02 #> 39  0.771150422 2.265235e-01 #> 40  0.997960094 1.694966e-03 #> 41  0.992507874 4.161436e-03 #> 42  0.146925900 8.413062e-01 #> 43  0.929285621 6.949892e-02 #> 44  0.946393484 3.039071e-02 #> 45  0.848161656 1.509286e-01 #> 46  0.765582021 2.281188e-01 #> 47  0.906464208 9.084652e-02 #> 48  0.931348538 6.843763e-02 #> 49  0.948921305 5.006041e-02 #> 50  0.976803096 2.313478e-02 #> 51  0.277995466 6.082479e-01 #> 52  0.311683132 6.830184e-01 #> 53  0.664146963 2.635440e-01 #> 54  0.249361414 7.479428e-01 #> 55  0.878241702 1.198537e-01 #> 56  0.637260992 2.996949e-01 #> 57  0.210729871 7.863598e-01 #> 58  0.027308118 9.694709e-01 #> 59  0.758623302 8.355378e-02 #> 60  0.387926000 5.709851e-01 #> 61  0.086194377 9.132535e-01 #> 62  0.926162213 2.261503e-02 #> 63  0.128022840 8.642691e-01 #> 64  0.898327863 2.723157e-02 #> 65  0.130964651 6.612032e-01 #> 66  0.593595505 3.673229e-01 #> 67  0.958380776 4.024891e-02 #> 68  0.090772461 8.087892e-01 #> 69  0.420435646 5.779399e-01 #> 70  0.133260226 8.629393e-01 #> 71  0.504882110 4.839482e-01 #> 72  0.406578554 5.915782e-01 #> 73  0.645293645 3.478754e-01 #> 74  0.519247169 2.737241e-01 #> 75  0.734870489 2.095489e-01 #> 76  0.958138539 1.461335e-02 #> 77  0.812191218 1.264981e-01 #> 78  0.912896321 8.514938e-02 #> 79  0.978657862 2.119510e-02 #> 80  0.016044824 9.836341e-01 #> 81  0.122144323 8.775790e-01 #> 82  0.070309187 9.272035e-01 #> 83  0.194938448 8.048312e-01 #> 84  0.891022209 9.624269e-02 #> 85  0.958380776 4.024891e-02 #> 86  0.059437245 9.401643e-01 #> 87  0.672034771 2.953945e-01 #> 88  0.330736771 6.674844e-01 #> 89  0.946744678 2.357189e-02 #> 90  0.277285564 7.201808e-01 #> 91  0.342463747 6.225436e-01 #> 92  0.891508689 2.394109e-02 #> 93  0.221487022 7.770072e-01 #> 94  0.039508517 9.572545e-01 #> 95  0.426831711 5.705601e-01 #> 96  0.490691231 6.496627e-02 #> 97  0.698527791 2.762532e-01 #> 98  0.734870489 2.095489e-01 #> 99  0.009680752 9.445952e-01 #> 100 0.467996371 5.314018e-01 #> 101 0.576108690 4.111629e-01 #> 102 0.961696898 2.861893e-02 #> 103 0.877177165 1.221776e-01 #> 104 0.958249977 2.284791e-02 #> 105 0.870879776 1.263950e-01 #> 106 0.851672810 1.206619e-01 #> 107 0.657810722 3.183482e-01 #> 108 0.886186912 3.351128e-02 #> 109 0.885499558 1.014339e-01 #> 110 0.321916434 6.735496e-01 #> 111 0.577480507 4.061726e-01 #> 112 0.978244776 2.023845e-02 #> 113 0.873487112 1.201395e-01 #> 114 0.848034409 1.236129e-01 #> 115 0.885718143 1.085828e-02 #> 116 0.607788840 3.468240e-01 #> 117 0.884632536 9.989009e-02 #> 118 0.191705611 7.884282e-01 #> 119 0.992440547 2.385649e-06 #> 120 0.508966080 4.875811e-01 #> 121 0.651305390 3.369285e-01 #> 122 0.949464886 1.861772e-04 #> 123 0.939398567 1.451656e-02 #> 124 0.929464957 6.026831e-02 #> 125 0.513528277 4.864713e-01 #> 126 0.594651369 3.400324e-01 #> 127 0.968111697 1.198353e-02 #> 128 0.898455967 9.317487e-02 #> 129 0.989867285 6.049073e-03 #> 130 0.787377903 7.679059e-02 #> 131 0.967682818 4.416571e-03 #> 132 0.132869218 8.353566e-01 #> 133 0.977445867 9.815525e-03 #> 134 0.899008289 4.428491e-02 #> 135 0.716813201 1.631402e-01 #> 136 0.867820065 1.312456e-01 #> 137 0.428750425 5.485781e-01 #> 138 0.748409494 2.333771e-01 #> 139 0.890507966 9.063963e-02 #> 140 0.754859896 2.349616e-01 #> 141 0.744129523 2.221864e-01 #> 142 0.695525700 2.244282e-01 #> 143 0.961696898 2.861893e-02 #> 144 0.666268263 3.299894e-01 #> 145 0.547542725 4.211977e-01 #> 146 0.810397292 1.215923e-01 #> 147 0.830568366 1.546654e-01 #> 148 0.874671814 1.118173e-01 #> 149 0.391519529 5.835188e-01 #> 150 0.903130625 9.679300e-02 #> 8   0.997960094 1.694966e-03 #> 9   0.666045138 3.327885e-01 #> 10  0.856123843 1.417503e-01 #>  #> $masses #>           1           2           3           4           5           6  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>           7          14          15          16          17          18  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          19          20          21          22          23          24  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          25          26          27          28          29          30  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          31          32          33          34          35          36  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          37          38          39          40          41          42  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          43          44          45          46          47          48  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          49          50          51          52          53          54  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          55          56          57          58          59          60  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          61          62          63          64          65          66  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          67          68          69          70          71          72  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          73          74          75          76          77          78  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          79          80          81          82          83          84  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          85          86          87          88          89          90  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          91          92          93          94          95          96  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          97          98          99         100         101         102  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         103         104         105         106         107         108  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         109         110         111         112         113         114  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         115         116         117         118         119         120  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         121         122         123         124         125         126  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         127         128         129         130         131         132  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         133         134         135         136         137         138  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         139         140         141         142         143         144  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         145         146         147         148         149         150  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>           8           9          10  #> 0.006802721 0.006802721 0.006802721  #>  #> $supplement #>   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [145] FALSE FALSE FALSE  TRUE  TRUE  TRUE #>"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Object Summaries — summary","title":"Object Summaries — summary","text":"Provides summary results multivariate data analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Object Summaries — summary","text":"","code":"# S4 method for CA summary(object, margin = 1, active = TRUE, sup = TRUE, rank = 3)  # S4 method for PCA summary(object, margin = 1, active = TRUE, sup = TRUE, rank = 3)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Object Summaries — summary","text":"object CA PCA object. margin length-one numeric vector giving subscript data summarized: 1 indicates individuals/rows (default), 2 indicates variables/columns. active logical scalar: active observations summarized? sup logical scalar: supplementary observations summarized? rank integer value specifying maximal number components kept results.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Object Summaries — summary","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Object Summaries — summary","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## Compute correspondence analysis X <- ca(colours)  ## Rows summary summary(X, margin = 1) #> --- Correspondence Analysis (CA) ------------------------------------------------ #>  #> Eigenvalues: #>    eigenvalues variance cumulative #> F1       0.209   89.373     89.373 #> F2       0.022    9.515     98.888 #> F3       0.003    1.112    100.000 #>  #> Active rows: #>          inertia CA1_coord CA1_contrib CA1_cos2 CA2_coord CA2_contrib CA2_cos2 #> marron    93.086    -0.492      43.116    0.967    -0.088      13.042    0.031 #> noisette  13.089    -0.213       3.401    0.542     0.167      19.804    0.336 #> vert      16.085     0.162       1.355    0.176     0.339      55.910    0.773 #> bleu     111.337     0.547      52.128    0.977    -0.083      11.244    0.022 #>          CA3_coord CA3_contrib CA3_cos2 #> marron       0.022       6.680    0.002 #> noisette    -0.101      61.086    0.121 #> vert         0.088      31.925    0.052 #> bleu        -0.005       0.310    0.000  ## Columns summary summary(X, margin = 2) #> --- Correspondence Analysis (CA) ------------------------------------------------ #>  #> Eigenvalues: #>    eigenvalues variance cumulative #> F1       0.209   89.373     89.373 #> F2       0.022    9.515     98.888 #> F3       0.003    1.112    100.000 #>  #> Active columns: #>         inertia CA1_coord CA1_contrib CA1_cos2 CA2_coord CA2_contrib CA2_cos2 #> brun     55.425    -0.505      22.246    0.838    -0.215      37.877    0.152 #> chatain  12.284    -0.148       5.086    0.864     0.033       2.319    0.042 #> roux     15.095    -0.130       0.964    0.133     0.320      55.131    0.812 #> blond   150.793     0.835      71.704    0.993    -0.070       4.673    0.007 #>         CA3_coord CA3_contrib CA3_cos2 #> brun        0.056      21.633    0.010 #> chatain    -0.049      44.284    0.094 #> roux        0.083      31.913    0.055 #> blond       0.016       2.171    0.000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Coordinates — tidy","title":"Tidy Coordinates — tidy","text":"Tidy Coordinates","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Coordinates — tidy","text":"","code":"tidy(x, ...)  augment(x, ...)  # S4 method for MultivariateAnalysis augment(x, margin = 1, axes = c(1, 2), principal = TRUE, ...)  # S4 method for MultivariateAnalysis tidy(x, margin = 1, principal = TRUE, ...)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Coordinates — tidy","text":"x CA PCA object. ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. principal logical scalar: principal coordinates returned? FALSE, standard coordinates returned.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Coordinates — tidy","text":"tidy() returns long data.frame following columns: label Row/column names original data. component Component. supplementary Whether observation active supplementary. coordinate Coordinates. contribution Contributions definition components. cos2 \\(cos^2\\). augment() returns wide data.frame row/column coordinates along axes following columns: label Row/column names original data. supplementary Whether observation active supplementary. mass Weight/mass observation. sum Sum squared coordinates along axes. contribution Joint contributions definition axes. cos2 Joint \\(cos^2\\) along axes.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Coordinates — tidy","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Coordinates — tidy","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 5:10) #> 1 qualitative variable was removed: Species.  ## Get row principal coordinates head(get_coordinates(X, margin = 1, principal = TRUE)) #>           F1         F2          F3  .sup #> 1  -2.385846  0.5084067 -0.12383101 FALSE #> 2  -2.215654 -0.6515155 -0.23313750 FALSE #> 3  -2.496566 -0.3169280  0.04974232 FALSE #> 4  -2.433607 -0.5744432  0.09690301 FALSE #> 11 -2.279483  1.0742447 -0.26629760 FALSE #> 12 -2.450871  0.1591839  0.09988982 FALSE  ## Get row standard coordinates head(get_coordinates(X, margin = 1, principal = FALSE)) #>          F1         F2         F3  .sup #> 1 -1.403075  0.5268995 -0.3142569 FALSE #> 2 -1.302987 -0.6752138 -0.5916536 FALSE #> 3 -1.468187 -0.3284560  0.1262355 FALSE #> 4 -1.431162 -0.5953380  0.2459193 FALSE #> 5 -1.340525  1.1133193 -0.6758069 FALSE #> 6 -1.441315  0.1649741  0.2534992 FALSE  ## Tidy principal coordinates head(tidy(X, margin = 1)) #>   label component supplementary coordinate contribution        cos2 #> 1     1        F1         FALSE -2.3858463   1.36709655 0.954033073 #> 2     1        F2         FALSE  0.5084067   0.19279380 0.043321256 #> 3     1        F3         FALSE -0.1238310   0.06858151 0.002570025 #> 4    10        F1          TRUE -2.3173283           NA 0.953213276 #> 5    10        F2          TRUE -0.4448514           NA 0.035127301 #> 6    10        F3          TRUE -0.2525218           NA 0.011319124 head(tidy(X, margin = 2)) #>          label component supplementary coordinate contribution         cos2 #> 1 Petal.Length        F1         FALSE 0.99121597  33.97921607 0.9825091061 #> 2 Petal.Length        F2         FALSE 0.01655680   0.02944324 0.0002741275 #> 3 Petal.Length        F3         FALSE 0.05477927   1.93260521 0.0030007681 #> 4  Petal.Width        F1         FALSE 0.96350955  32.10619376 0.9283506618 #> 5  Petal.Width        F2         FALSE 0.05756108   0.35586957 0.0033132775 #> 6  Petal.Width        F3         FALSE 0.24948796  40.08758618 0.0622442430  head(augment(X, margin = 1, axes = c(1, 2))) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.385846  0.5084067     1         FALSE 0.006944444 5.950740     4.132458 #> 2 -2.215654 -0.6515155     2         FALSE 0.006944444 5.333593     3.703884 #> 3 -2.496566 -0.3169280     3         FALSE 0.006944444 6.333284     4.398114 #> 4 -2.433607 -0.5744432     4         FALSE 0.006944444 6.252426     4.341962 #> 5 -2.279483  1.0742447    11         FALSE          NA 6.350046     4.409754 #> 6 -2.450871  0.1591839    12         FALSE          NA 6.032107     4.188963 #>        cos2 #> 1 0.9973543 #> 2 0.9880287 #> 3 0.9995093 #> 4 0.9977072 #> 5 0.9889258 #> 6 0.9951651 head(augment(X, margin = 2, axes = c(1, 2))) #>           F1         F2        label supplementary mass       sum contribution #> 1  0.8875399 0.36087785 Sepal.Length         FALSE    1 0.9179599     91.79599 #> 2 -0.4392190 0.89286998  Sepal.Width         FALSE    1 0.9901302     99.01302 #> 3  0.9912160 0.01655680 Petal.Length         FALSE    1 0.9827832     98.27832 #> 4  0.9635096 0.05756108  Petal.Width         FALSE    1 0.9316639     93.16639 #>        cos2 #> 1 0.9179599 #> 2 0.9901302 #> 3 0.9827832 #> 4 0.9316639"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Contributions and cos2 — viz_contributions","title":"Visualize Contributions and cos2 — viz_contributions","text":"Plots contributions histogram \\(cos^2\\) scatterplot.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Contributions and cos2 — viz_contributions","text":"","code":"viz_contributions(x, ...)  viz_cos2(x, ...)  # S4 method for MultivariateAnalysis viz_contributions(   x,   margin = 2,   axes = 1,   sort = TRUE,   decreasing = TRUE,   limit = 10,   horiz = FALSE,   col = \"grey90\",   border = \"grey10\",   ... )  # S4 method for MultivariateAnalysis viz_cos2(   x,   margin = 2,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   sort = TRUE,   decreasing = TRUE,   limit = 10,   horiz = FALSE,   col = \"grey90\",   border = \"grey10\",   ... )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Contributions and cos2 — viz_contributions","text":"x CA PCA object. ... Extra parameters passed graphics::barplot(). margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-one numeric vector giving dimensions plotted. sort logical scalar: data sorted? decreasing logical scalar: sort order decreasing? used sort TRUE. limit integer specifying number top elements displayed. horiz logical scalar: bars drawn horizontally first bottom? col, border character string specifying bars infilling border colors. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted?","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Contributions and cos2 — viz_contributions","text":"viz_contributions() viz_cos2() called side-effects: result graphic displayed. Invisibly return x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Contributions and cos2 — viz_contributions","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Contributions and cos2 — viz_contributions","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Get row contributions head(get_contributions(X, margin = 1)) #>          F1         F2          F3 #> 1 1.1715796 0.16806554 0.074085470 #> 2 0.9891845 0.33146674 0.250034006 #> 3 1.2768164 0.08526419 0.008875320 #> 4 1.2077372 0.26029781 0.037858004 #> 5 1.3046313 0.30516562 0.001125175 #> 6 0.9841236 1.61748779 0.003303827  ## Plot contributions viz_contributions(X)   ## Plot cos2 viz_cos2(X)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Individuals Factor Map — viz_individuals","title":"Visualize Individuals Factor Map — viz_individuals","text":"Plots row/individual principal coordinates.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Individuals Factor Map — viz_individuals","text":"","code":"viz_individuals(x, ...)  viz_rows(x, ...)  # S4 method for MultivariateAnalysis viz_rows(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapCA viz_rows(x, axes = c(1, 2), ...)  # S4 method for PCA viz_individuals(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Individuals Factor Map — viz_individuals","text":"x CA PCA object. ... graphical parameters (see details). axes length-two numeric vector giving dimensions plotted. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels logical scalar: labels drawn? highlight vector specifying information highlighted. NULL (default), highlighting applied. mapped least one graphical parameters explicitly specified (see examples). single character string passed, must one \"observation\", \"mass\", \"sum\", \"contribution\" \"cos2\" (see augment()). unambiguous substring can given. xlim length-two numeric vector giving x limits plot. default value, NULL, indicates range finite values plotted used. ylim length-two numeric vector giving y limits plot. default value, NULL, indicates range finite values plotted used. main character string giving main title plot. sub character string giving subtitle 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/dimensio/dev/reference/viz_individuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Individuals Factor Map — viz_individuals","text":"viz_*() called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize Individuals Factor Map — viz_individuals","text":"Commonly used graphical parameters : pch vector plotting characters symbols. can either single character integer code one set graphics symbols. cex numerical vector giving amount plotting characters symbols scaled relative default. col colors lines points. Multiple colors can specified point can given color. bg background color open plot symbols given pch = 21:25.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize Individuals Factor Map — viz_individuals","text":"careful: graphical parameters silently recycled.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Individuals Factor Map — viz_individuals","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Individuals Factor Map — viz_individuals","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Plot individuals viz_individuals(X, panel.last = graphics::grid())   ## Plot variables viz_variables(X, panel.last = graphics::grid())   ## Graphical parameters ## Continuous values viz_individuals(X, highlight = iris$Petal.Length, pch = 16)  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"))  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"),                 cex = c(1, 2))   viz_variables(X, highlight = \"contribution\",               col = grDevices::hcl.colors(12, \"BluGrn\", rev = TRUE),               lwd = c(1, 5))   ## Discrete values viz_individuals(X, highlight = iris$Species, pch = 21:23)  viz_individuals(X, highlight = iris$Species, pch = 21:23,                 bg = c(\"#004488\", \"#DDAA33\", \"#BB5566\"),                 col = \"black\")   viz_variables(X, highlight = c(\"Petal\", \"Petal\", \"Sepal\", \"Sepal\"),               col = c(\"#EE7733\", \"#0077BB\"),               lty = c(1, 3))"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Variables Factor Map — viz_variables","title":"Visualize Variables Factor Map — viz_variables","text":"Plots column/variable principal coordinates.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Variables Factor Map — viz_variables","text":"","code":"viz_variables(x, ...)  viz_columns(x, ...)  # S4 method for MultivariateAnalysis viz_columns(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapCA viz_columns(x, axes = c(1, 2), ...)  # S4 method for PCA viz_variables(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = TRUE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapPCA viz_variables(x, axes = c(1, 2), ...)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Variables Factor Map — viz_variables","text":"x CA PCA object. ... graphical parameters (see details). axes length-two numeric vector giving dimensions plotted. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels logical scalar: labels drawn? highlight vector specifying information highlighted. NULL (default), highlighting applied. mapped least one graphical parameters explicitly specified (see examples). single character string passed, must one \"observation\", \"mass\", \"sum\", \"contribution\" \"cos2\" (see augment()). unambiguous substring can given. xlim length-two numeric vector giving x limits plot. default value, NULL, indicates range finite values plotted used. ylim length-two numeric vector giving y limits plot. default value, NULL, indicates range finite values plotted used. main character string giving main title plot. sub character string giving subtitle 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/dimensio/dev/reference/viz_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Variables Factor Map — viz_variables","text":"viz_*() called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize Variables Factor Map — viz_variables","text":"Commonly used graphical parameters : pch vector plotting characters symbols. can either single character integer code one set graphics symbols. cex numerical vector giving amount plotting characters symbols scaled relative default. lty vector line types. lwd vector line widths. col colors lines points. Multiple colors can specified point can given color. bg background color open plot symbols given pch = 21:25.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize Variables Factor Map — viz_variables","text":"careful: graphical parameters silently recycled.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Variables Factor Map — viz_variables","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Variables Factor Map — viz_variables","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Plot individuals viz_individuals(X, panel.last = graphics::grid())   ## Plot variables viz_variables(X, panel.last = graphics::grid())   ## Graphical parameters ## Continuous values viz_individuals(X, highlight = iris$Petal.Length, pch = 16)  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"))  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"),                 cex = c(1, 2))   viz_variables(X, highlight = \"contribution\",               col = grDevices::hcl.colors(12, \"BluGrn\", rev = TRUE),               lwd = c(1, 5))   ## Discrete values viz_individuals(X, highlight = iris$Species, pch = 21:23)  viz_individuals(X, highlight = iris$Species, pch = 21:23,                 bg = c(\"#004488\", \"#DDAA33\", \"#BB5566\"),                 col = \"black\")   viz_variables(X, highlight = c(\"Petal\", \"Petal\", \"Sepal\", \"Sepal\"),               col = c(\"#EE7733\", \"#0077BB\"),               lty = c(1, 3))"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Envelopes — viz_wrap","title":"Plot Envelopes — viz_wrap","text":"Plot Envelopes","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Envelopes — viz_wrap","text":"","code":"viz_hull(x, ...)  viz_confidence(x, ...)  viz_tolerance(x, ...)  # S4 method for MultivariateAnalysis viz_tolerance(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95, ...)  # S4 method for BootstrapCA viz_tolerance(x, margin = 1, axes = c(1, 2), level = 0.95, ...)  # S4 method for MultivariateAnalysis viz_confidence(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95, ...)  # S4 method for BootstrapCA viz_confidence(x, margin = 1, axes = c(1, 2), level = 0.95, ...)  # S4 method for MultivariateAnalysis viz_hull(x, margin = 1, axes = c(1, 2), group = NULL, ...)  # S4 method for BootstrapCA viz_hull(x, margin = 1, axes = c(1, 2), ...)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Envelopes — viz_wrap","text":"x object wrap observations (CA PCA object). ... graphical parameters passed graphics::polygon(). margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. group vector specifying group observation belongs . level numeric vector specifying confidence/tolerance level.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Envelopes — viz_wrap","text":"viz_*()called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Envelopes — viz_wrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Envelopes — viz_wrap","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Convex hull coordinates hulls <- wrap_hull(X, margin = 1, group = iris$Species)  ## Confidence ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species,                         level = c(0.68, 0.95))  ## Tolerance ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species, level = 0.95)  ## Plot with convex hulls col <- c(\"#004488\", \"#DDAA33\", \"#BB5566\") viz_rows(X, highlight = iris$Species, col = col) viz_hull(X, group = iris$Species, border = col)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrap Observations — wrap","title":"Wrap Observations — wrap","text":"wrap_hull() computes convex hull set observations. wrap_confidence() computes confidence ellipse. wrap_tolerance() computes tolerance ellipse.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrap Observations — wrap","text":"","code":"wrap_hull(x, ...)  wrap_confidence(x, ...)  wrap_tolerance(x, ...)  # S4 method for MultivariateAnalysis wrap_confidence(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95)  # S4 method for MultivariateAnalysis wrap_tolerance(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95)  # S4 method for MultivariateAnalysis wrap_hull(x, margin = 1, axes = c(1, 2), group = NULL)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrap Observations — wrap","text":"x object wrap observations (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. group vector specifying group observation belongs . level numeric vector specifying confidence/tolerance level.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrap Observations — wrap","text":"wrap_*() returns data.frame envelope x y coordinates. extra column named group added specifying group observation belongs .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Wrap Observations — wrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrap Observations — wrap","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Convex hull coordinates hulls <- wrap_hull(X, margin = 1, group = iris$Species)  ## Confidence ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species,                         level = c(0.68, 0.95))  ## Tolerance ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species, level = 0.95)  ## Plot with convex hulls col <- c(\"#004488\", \"#DDAA33\", \"#BB5566\") viz_rows(X, highlight = iris$Species, col = col) viz_hull(X, group = iris$Species, border = col)"}]
+[{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"do-pca","dir":"Articles","previous_headings":"","what":"Do PCA","title":"Principal Components Analysis","text":"","code":"## Load data data(iris) head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa  ## Compute PCA ## (non numeric variables are automatically removed) X <- pca(iris, center = TRUE, scale = TRUE) #> 1 qualitative variable was removed: Species."},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"explore-the-results","dir":"Articles","previous_headings":"","what":"Explore the results","title":"Principal Components Analysis","text":"dimensio provides several methods extract (get_*()) results: get_data() returns original data. get_contributions() returns contributions definition principal dimensions. get_coordinates() returns principal standard coordinates. get_correlations() returns correlations variables dimensions. get_cos2() returns cos2 values (.e. quality representation points factor map). get_eigenvalues() returns eigenvalues, percentages variance cumulative percentages variance. package also allows quickly visualize (viz_*()) results: biplot() produces biplot. screeplot() produces scree plot. viz_rows()/viz_individuals() displays row/individual principal coordinates. viz_columns()/viz_variables() displays columns/variable principal coordinates. viz_contributions() displays (joint) contributions. viz_cos2() displays (joint) cos2.","code":"## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.9184978 73.342264   73.34226 #> F2   0.9140305 22.969715   96.31198 #> F3   0.1467569  3.688021  100.00000  ## Scree plot screeplot(X, cumulative = TRUE)  ## Plot variable contributions to the definition of the first two axes viz_contributions(X, margin = 2, axes = c(1, 2))"},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"pca-biplot","dir":"Articles","previous_headings":"","what":"PCA biplot","title":"Principal Components Analysis","text":"biplot simultaneous representation rows columns rectangular dataset. generalization scatterplot case mutlivariate data: allows visualize much information possible single graph (Greenacre 2010). dimensio allows display two types biplots: form biplot (row-metric-preserving biplot) covariance biplot (column-metric-preserving biplot). See Greenacre (2010) details biplots. form biplot favors representation individuals: distance individuals approximates Euclidean distance rows. form biplot length vector approximates quality representation variable.  covariance biplot favors representation variables: length vector approximates standard deviation variable cosine angle formed two vectors approximates correlation two variables (Greenacre 2010). covariance biplot distance individuals approximates Mahalanobis distance rows.  Biplots drawbacks advantages: can quickly become difficult read display lot information . may preferable visualize results individuals variables separately.","code":"biplot(X, type = \"form\", label = \"variables\") biplot(X, type = \"covariance\", label = \"variables\")"},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"plot-pca-loadings","dir":"Articles","previous_headings":"","what":"Plot PCA loadings","title":"Principal Components Analysis","text":"viz_variables() depicts variables rays emanating origin (lengths directions important interpretation).  viz_variables() allows highlight additional information varying different graphical elements (color, transparency, shape size symbols…).","code":"## Plot variables factor map viz_variables(X) ## Highlight cos2 viz_variables(   x = X,    highlight = \"cos2\",    col = khroma::color(\"YlOrBr\")(4, range = c(0.5, 1)) )"},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"plot-pca-scores","dir":"Articles","previous_headings":"","what":"Plot PCA scores","title":"Principal Components Analysis","text":"viz_individuals() allows display individuals highlight additional information.","code":"## Plot individuals and color by species viz_individuals(   x = X,   highlight = iris$Species,   col = khroma::color(\"bright\")(3), # Custom color scale   pch = c(15, 16, 17) # Custom symbols ) ## Add ellipses viz_individuals(x = X) viz_tolerance(x = X, group = iris$Species, level = 0.95,               border = khroma::color(\"high contrast\")(3))  ## Add convex hull viz_individuals(x = X) viz_hull(x = X, group = iris$Species, level = 0.95,          border = khroma::color(\"high contrast\")(3)) ## Highlight petal length viz_individuals(   x = X,    highlight = iris$Petal.Length,   col = khroma::color(\"YlOrBr\")(12), # Custom color scale   cex = c(1, 2), # Custom size scale   pch = 16 ) ## Highlight contributions viz_individuals(   x = X,    highlight = \"contrib\",   col = khroma::color(\"iridescent\")(12), # Custom color scale   cex = c(1, 2), # Custom size scale   pch = 16 )"},{"path":"https://packages.tesselle.org/dimensio/dev/articles/pca.html","id":"custom-plot","dir":"Articles","previous_headings":"","what":"Custom plot","title":"Principal Components Analysis","text":"need flexibility, get_*() family tidy() augment() functions allow extract results data frames thus build custom graphs base graphics ggplot2.","code":"iris_tidy <- tidy(X, margin = 2) head(iris_tidy) #>          label component supplementary coordinate contribution        cos2 #> 1 Petal.Length        F1         FALSE 0.99155518  33.68793618 0.983181682 #> 2 Petal.Length        F2         FALSE 0.02341519   0.05998389 0.000548271 #> 3 Petal.Length        F3         FALSE 0.05444699   2.01999049 0.002964475 #> 4  Petal.Width        F1         FALSE 0.96497896  31.90629060 0.931184395 #> 5  Petal.Width        F2         FALSE 0.06399985   0.44812296 0.004095980 #> 6  Petal.Width        F3         FALSE 0.24298265  40.23019050 0.059040571  iris_augment <- augment(X, margin = 1) head(iris_augment) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.264703  0.4800266     1         FALSE 0.006666667 5.359304     3.572870 #> 2 -2.080961 -0.6741336     2         FALSE 0.006666667 4.784855     3.189904 #> 3 -2.364229 -0.3419080     3         FALSE 0.006666667 5.706480     3.804320 #> 4 -2.299384 -0.5973945     4         FALSE 0.006666667 5.644048     3.762699 #> 5 -2.389842  0.6468354     5         FALSE 0.006666667 6.129742     4.086494 #> 6 -2.075631  1.4891775     6         FALSE 0.006666667 6.525894     4.350596 #>        cos2 #> 1 0.9968578 #> 2 0.9864650 #> 3 0.9995167 #> 4 0.9977577 #> 5 0.9997491 #> 6 0.9998819 ## Custom plot with ggplot2 ggplot2::ggplot(data = iris_augment) +   ggplot2::aes(x = F1, y = F2, colour = contribution) +   ggplot2::geom_vline(xintercept = 0, linewidth = 0.5, linetype = \"dashed\") +   ggplot2::geom_hline(yintercept = 0, linewidth = 0.5, linetype = \"dashed\") +   ggplot2::geom_point() +   ggplot2::coord_fixed() + # /!\\   ggplot2::theme_bw() +   khroma::scale_color_iridescent()"},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Multivariate Data Analysis","text":"Simple Principal Components Analysis (PCA; see vignette(\"pca\")) Correspondence Analysis (CA) based Singular Value Decomposition (SVD). package provides S4 classes methods compute, extract, summarize visualize results multivariate data analysis. also includes methods partial bootstrap validation. many good packages multivariate data analysis (FactoMineR, ade4, vegan ca, extended FactoExtra). dimensio designed simple possible, providing necessary tools explore results analysis.","code":"To cite dimensio in publications use:    Frerebeau N (2023). _dimensio: Multivariate Data Analysis_.   Université Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.4478530 <https://doi.org/10.5281/zenodo.4478530>,   R package version 0.4.0, <https://packages.tesselle.org/dimensio/>.  Une entrée BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{dimensio: Multivariate Data Analysis}},     year = {2023},     organization = {Université Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.4.0},     doi = {10.5281/zenodo.4478530},     url = {https://packages.tesselle.org/dimensio/},   }  This package is a part of the tesselle project <https://www.tesselle.org>."},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Multivariate Data Analysis","text":"can install released version dimensio CRAN : development version GitHub :","code":"install.packages(\"dimensio\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/dimensio\")"},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Multivariate Data Analysis","text":"","code":"## Install extra packages (if needed) # install.packages(\"khroma\")  ## Load packages library(dimensio)"},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"compute","dir":"","previous_headings":"Usage","what":"Compute","title":"Multivariate Data Analysis","text":"","code":"## Load data data(iris)  ## Compute PCA ## (non numeric variables are automatically removed) X <- pca(iris, center = TRUE, scale = TRUE) #> 1 qualitative variable was removed: Species."},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"extract","dir":"","previous_headings":"Usage","what":"Extract","title":"Multivariate Data Analysis","text":"dimensio provides several methods extract results: get_data() returns original data. get_contributions() returns contributions definition principal dimensions. get_coordinates() returns principal standard coordinates. get_correlations() returns correlations variables dimensions. get_cos2() returns cos2 values (.e. quality representation points factor map). get_eigenvalues() returns eigenvalues, percentages variance cumulative percentages variance.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"visualize","dir":"","previous_headings":"Usage","what":"Visualize","title":"Multivariate Data Analysis","text":"package allows quickly visualize results: biplot() produces biplot. screeplot() produces scree plot. viz_rows()/viz_individuals() displays row/individual principal coordinates. viz_columns()/viz_variables() displays columns/variable principal coordinates. viz_variables() depicts variables rays emanating origin (lengths directions important interpretation). viz_contributions() displays (joint) contributions. viz_cos2() displays (joint) cos2. viz_*() functions allow highlight additional information varying different graphical elements (color, transparency, shape size symbols…).","code":"## Form biplot biplot(X, type = \"form\") ## Highlight species viz_individuals(   x = X,    highlight = iris$Species,    col = khroma::color(\"high contrast\")(3),    pch = 16 ) ## Add ellipses viz_tolerance(   x = X,    group = iris$Species,    level = 0.95,   border = khroma::color(\"high contrast\")(3) )  ## Highlight petal length viz_individuals(   x = X,   highlight = iris$Petal.Length,   col = khroma::color(\"iridescent\")(255),    cex = c(1, 2),   pch = 16 ) ## Plot variables factor map viz_variables(X)  ## Scree plot screeplot(X, eigenvalues = FALSE, cumulative = TRUE)"},{"path":"https://packages.tesselle.org/dimensio/dev/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Multivariate Data Analysis","text":"Please note dimensio project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/CA-class.html","id":null,"dir":"Reference","previous_headings":"","what":"CA Results — CA-class","title":"CA Results — CA-class","text":"S4 class store results simple correspondence analysis.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/CA-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"CA Results — CA-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/CA-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CA Results — CA-class","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## The chi square of independence between the two variables stats::chisq.test(colours) #>  #> \tPearson's Chi-squared test #>  #> data:  colours #> X-squared = 138.29, df = 9, p-value < 2.2e-16 #>   ## Compute correspondence analysis X <- ca(colours)  ## Plot rows viz_rows(X, labels = TRUE)   ## Plot columns viz_columns(X, labels = TRUE)   ## Get row coordinates head(get_coordinates(X, margin = 1)) #>                  F1          F2           F3  .sup #> marron   -0.4921577 -0.08832151  0.021611305 FALSE #> noisette -0.2125969  0.16739109 -0.100518284 FALSE #> vert      0.1617534  0.33903957  0.087597437 FALSE #> bleu      0.5474139 -0.08295428 -0.004709408 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                 F1          F2          F3  .sup #> brun    -0.5045624 -0.21482046  0.05550909 FALSE #> chatain -0.1482527  0.03266635 -0.04880414 FALSE #> roux    -0.1295233  0.31964240  0.08315117 FALSE #> blond    0.8353478 -0.06957934  0.01621471 FALSE  ## Get row distances to centroid head(get_distances(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.25048691 0.08332116 0.14878530 0.30656555   ## Get row inertias head(get_inertia(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.09308635 0.01308930 0.01608490 0.11133715   ## Get row contributions head(get_contributions(X, margin = 1)) #>                 F1       F2         F3 #> marron   43.115744 13.04249  6.6795992 #> noisette  3.400961 19.80398 61.0855951 #> vert      1.354851 55.90952 31.9248234 #> bleu     52.128445 11.24401  0.3099822  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1 0.208772652 89.372732   89.37273 #> F2 0.022226615  9.514911   98.88764 #> F3 0.002598439  1.112356  100.00000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateAnalysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Output of Multivariate Data Analysis — MultivariateAnalysis","title":"Output of Multivariate Data Analysis — MultivariateAnalysis","text":"virtual S4 class store output multivariate data analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateAnalysis.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Output of Multivariate Data Analysis — MultivariateAnalysis","text":"data numeric matrix. dimension integer giving dimension solution. singular_values numeric vector giving singular values. rows MultivariateResults object. columns MultivariateResults object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateAnalysis.html","id":"subset","dir":"Reference","previous_headings":"","what":"Subset","title":"Output of Multivariate Data Analysis — MultivariateAnalysis","text":"code snippets , x MultivariateAnalysis object. x[[]] Extracts information slot selected subscript . length-one character vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateAnalysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Output of Multivariate Data Analysis — MultivariateAnalysis","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateBootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Output of Bootstrap Replications — MultivariateBootstrap","title":"Output of Bootstrap Replications — MultivariateBootstrap","text":"virtual S4 class store output bootstrap analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateBootstrap.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Output of Bootstrap Replications — MultivariateBootstrap","text":"replications integer giving number bootstrap replications.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateBootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Output of Bootstrap Replications — MultivariateBootstrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateResults.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Data Analysis Results — MultivariateResults","title":"Multivariate Data Analysis Results — MultivariateResults","text":"S4 class store results multivariate data analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateResults.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Multivariate Data Analysis Results — MultivariateResults","text":"names character vector specifying row names. principal numeric matrix giving principal coordinates. standard numeric matrix giving standard coordinates. contributions numeric matrix giving contributions definition dimensions. cosine numeric matrix giving \\(cos^2\\) values. distances numeric vector giving distances centroid. weights numeric vector giving masses/weights. supplement logical vector specifying supplementary points. order integer vector giving original indices data (computation moves supplementary points end results). groups character vector specifying class observation.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateResults.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Multivariate Data Analysis Results — MultivariateResults","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateSummary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of Multivariate Data Analysis — MultivariateSummary","title":"Summary of Multivariate Data Analysis — MultivariateSummary","text":"virtual S4 class store summary multivariate data analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateSummary.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Summary of Multivariate Data Analysis — MultivariateSummary","text":"data numeric matrix. eigenvalues numeric matrix. results numeric matrix. supplement logical vector specifying supplementary points. margin integer.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/MultivariateSummary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary of Multivariate Data Analysis — MultivariateSummary","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/PCA-class.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Results — PCA-class","title":"PCA Results — PCA-class","text":"S4 class store results principal components analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/PCA-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"PCA Results — PCA-class","text":"center numeric vector giving column mean initial dataset (active individuals ). scale numeric vector giving column standard deviations initial dataset (active individuals ).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/PCA-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"PCA Results — PCA-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/PCA-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Results — PCA-class","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2) #> 1 qualitative variable was removed: Species.  ## Get row coordinates head(get_coordinates(X, margin = 1)) #>          F1          F2  .sup #> 1 -1.974826  0.27679945 FALSE #> 2 -2.107853  0.08452378 FALSE #> 3 -2.273786 -0.09394418 FALSE #> 4 -2.274485 -0.21769741 FALSE #> 5 -2.041339  0.18066161 FALSE #> 6 -1.526095  0.38607867 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                      F1         F2  .sup #> Sepal.Length  0.9347789  0.3540256 FALSE #> Petal.Length  0.9853020 -0.1083268 FALSE #> Petal.Width   0.9669170 -0.2318721 FALSE #> Sepal.Width  -0.3178714  0.4453379  TRUE  ## Get correlations between variables and dimensions head(get_correlations(X)) #>                      F1         F2  .sup #> Sepal.Length  0.9347789  0.3540256 FALSE #> Petal.Length  0.9853020 -0.1083268 FALSE #> Petal.Width   0.9669170 -0.2318721 FALSE #> Sepal.Width  -0.3178714  0.4453379  TRUE  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.7795601 93.575482   93.57548 #> F2   0.1908335  6.424518  100.00000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/benthos.html","id":null,"dir":"Reference","previous_headings":"","what":"Benthos — benthos","title":"Benthos — benthos","text":"Abundances Marine Species Sea-Bed Samples","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/benthos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Benthos — benthos","text":"","code":"benthos"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/benthos.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Benthos — benthos","text":"data.frame 13 columns (sites) 92 rows (species).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/benthos.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Benthos — benthos","text":"http://www.carme-n.org/?sec=data7","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Biplot — biplot","title":"Biplot — biplot","text":"Biplot","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biplot — biplot","text":"","code":"# S4 method for CA biplot(   x,   axes = c(1, 2),   type = c(\"rows\", \"columns\", \"contributions\"),   active = TRUE,   sup = TRUE,   labels = \"columns\",   col.rows = \"#004488\",   col.columns = \"#BB5566\",   cex.rows = graphics::par(\"cex\"),   cex.columns = graphics::par(\"cex\"),   pch.rows = 16,   pch.columns = 17,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   ... )  # S4 method for PCA biplot(   x,   axes = c(1, 2),   type = c(\"form\", \"covariance\"),   active = TRUE,   sup = TRUE,   labels = \"variables\",   col.rows = \"#004488\",   col.columns = \"#BB5566\",   pch.rows = 16,   pch.columns = 17,   lty = \"solid\",   lwd = 2,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   ... )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Biplot — biplot","text":"x CA PCA object. axes length-two numeric vector giving dimensions plotted. type character string specifying biplot plotted (see ). must one \"rows\", \"columns\", \"contribution\" (CA), \"form\" \"covariance\" (PCA). unambiguous substring can given. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels character vector specifying whether \"rows\"/\"individuals\" /\"columns\"/\"variables\" names must drawn. unambiguous substring can given. col.rows, col.columns color specification. cex.rows, cex.columns numerical vector giving amount plotting characters symbols scaled relative default. pch.rows, pch.columns symbol specification. xlim length-two numeric vector giving x limits plot. default value, NULL, indicates range finite values plotted used. ylim length-two numeric vector giving y limits plot. default value, NULL, indicates range finite values plotted used. main character string giving main title plot. sub character string giving subtitle plot. ... Currently used. lty, lwd specification line type width.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Biplot — biplot","text":"biplot() called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Biplot — biplot","text":"biplot simultaneous representation rows columns rectangular dataset. generalization scatterplot case mutlivariate data: allows visualize much information possible single graph (Greenacre 2010). Biplots drawbacks advantages: can quickly become difficult read display lot information . may preferable visualize results individuals variables separately.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"pca-biplots","dir":"Reference","previous_headings":"","what":"PCA Biplots","title":"Biplot — biplot","text":"form Form biplot (row-metric-preserving). form biplot favors representation individuals: distance individuals approximates Euclidean distance rows. form biplot length vector approximates quality representation variable. covariance Covariance biplot (column-metric-preserving). covariance biplot favors representation variables: length vector approximates standard deviation variable cosine angle formed two vectors approximates correlation two variables. covariance biplot distance individuals approximates Mahalanobis distance rows.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"ca-biplots","dir":"Reference","previous_headings":"","what":"CA Biplots","title":"Biplot — biplot","text":"rows Row principal biplot. columns Column principal biplot. contribution Contribution biplot","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Biplot — biplot","text":"Aitchison, J. Greenacre, M. (2002). Biplots Compositional Data. Journal Royal Statistical Society: Series C (Applied Statistics), 51(4): 375-92. doi:10.1111/1467-9876.00275 . Greenacre, M. J. Biplots Practice. Bilbao: Fundación BBVA, 2010.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Biplot — biplot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/biplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Biplot — biplot","text":"","code":"## Replicate examples from Greenacre 2007, p. 59-68 data(\"countries\")  ## Compute principal components analysis ## All rows and all columns obtain the same weight row_w <- rep(1 / nrow(countries), nrow(countries)) # 1/13 col_w <- rep(1 / ncol(countries), ncol(countries)) # 1/6 Y <- pca(countries, scale = FALSE, weight_row = row_w, weight_col = col_w)  ## Row-metric-preserving biplot (form biplot) biplot(Y, type = \"form\")   ## Column-metric-preserving biplot (covariance biplot) biplot(Y, type = \"covariance\")   ## Replicate examples from Greenacre 2007, p. 79-88 data(\"benthos\")  ## Compute correspondence analysis X <- ca(benthos)  ## Row principal CA biplot biplot(X, type = \"row\", labels = \"columns\")   ## Column principal CA biplot biplot(X, type = \"column\", labels = \"columns\")   ## Contribution CA biplot biplot(X, type = \"contrib\", labels = NULL)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial Bootstrap Analysis — bootstrap","title":"Partial Bootstrap Analysis — bootstrap","text":"Checks analysis partial bootstrap resampling.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial Bootstrap Analysis — bootstrap","text":"","code":"bootstrap(object, ...)  # S4 method for CA bootstrap(object, n = 30)  # S4 method for PCA bootstrap(object, n = 30)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial Bootstrap Analysis — bootstrap","text":"object CA PCA object. ... Currently used. n non-negative integer giving number bootstrap replications.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial Bootstrap Analysis — bootstrap","text":"#' Returns BootstrapCA BootstrapPCA object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial Bootstrap Analysis — bootstrap","text":"Greenacre, Michael J. Theory Applications Correspondence Analysis. London: Academic Press, 1984. Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial Bootstrap Analysis — bootstrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial Bootstrap Analysis — bootstrap","text":"","code":"## Bootstrap on CA ## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## Compute correspondence analysis X <- ca(colours)  ## Bootstrap (30 replicates) Y <- bootstrap(X, n = 30)  if (FALSE) { ## Get replicated coordinates get_replications(Y, margin = 1) get_replications(Y, margin = 2) }  ## Plot with ellipses viz_rows(Y) viz_tolerance(Y, margin = 1, level = c(0.68, 0.95))   viz_columns(Y) viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))   ## Plot with convex hulls viz_columns(Y) viz_hull(Y, margin = 2)   ## Bootstrap on PCA ## Compute principal components analysis data(\"iris\") X <- pca(iris) #> 1 qualitative variable was removed: Species.  ## Bootstrap (30 replicates) Y <- bootstrap(X, n = 30)  ## Plot with ellipses viz_variables(Y) viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":null,"dir":"Reference","previous_headings":"","what":"Correspondence Analysis — ca","title":"Correspondence Analysis — ca","text":"Computes simple correspondence analysis based singular value decomposition.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correspondence Analysis — ca","text":"","code":"ca(object, ...)  # S4 method for data.frame ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)  # S4 method for matrix ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correspondence Analysis — ca","text":"object \\(m \\times p\\) numeric matrix data.frame. ... Currently used. rank integer value specifying maximal number components kept results. NULL (default), \\(min(m, p) - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows. sup_col numeric logical vector specifying indices supplementary columns.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correspondence Analysis — ca","text":"CA object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correspondence Analysis — ca","text":"Greenacre, M. J. Theory Applications Correspondence Analysis. London: Academic Press, 1984. Greenacre, M. J. Correspondence Analysis Practice. Seconde edition. Interdisciplinary Statistics Series. Boca Raton: Chapman & Hall/CRC, 2007. Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correspondence Analysis — ca","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/ca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correspondence Analysis — ca","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## The chi square of independence between the two variables stats::chisq.test(colours) #>  #> \tPearson's Chi-squared test #>  #> data:  colours #> X-squared = 138.29, df = 9, p-value < 2.2e-16 #>   ## Compute correspondence analysis X <- ca(colours)  ## Plot rows viz_rows(X, labels = TRUE)   ## Plot columns viz_columns(X, labels = TRUE)   ## Get row coordinates head(get_coordinates(X, margin = 1)) #>                  F1          F2           F3  .sup #> marron   -0.4921577 -0.08832151  0.021611305 FALSE #> noisette -0.2125969  0.16739109 -0.100518284 FALSE #> vert      0.1617534  0.33903957  0.087597437 FALSE #> bleu      0.5474139 -0.08295428 -0.004709408 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                 F1          F2          F3  .sup #> brun    -0.5045624 -0.21482046  0.05550909 FALSE #> chatain -0.1482527  0.03266635 -0.04880414 FALSE #> roux    -0.1295233  0.31964240  0.08315117 FALSE #> blond    0.8353478 -0.06957934  0.01621471 FALSE  ## Get row distances to centroid head(get_distances(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.25048691 0.08332116 0.14878530 0.30656555   ## Get row inertias head(get_inertia(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.09308635 0.01308930 0.01608490 0.11133715   ## Get row contributions head(get_contributions(X, margin = 1)) #>                 F1       F2         F3 #> marron   43.115744 13.04249  6.6795992 #> noisette  3.400961 19.80398 61.0855951 #> vert      1.354851 55.90952 31.9248234 #> bleu     52.128445 11.24401  0.3099822  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1 0.208772652 89.372732   89.37273 #> F2 0.022226615  9.514911   98.88764 #> F3 0.002598439  1.112356  100.00000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/colours.html","id":null,"dir":"Reference","previous_headings":"","what":"Colours — colours","title":"Colours — colours","text":"Contingency table eye hair colours different individuals.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/colours.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Colours — colours","text":"","code":"colours"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/colours.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Colours — colours","text":"data.frame 4 columns (hair colours) 4 rows (eye colours).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/colours.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Colours — colours","text":"Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006, p. 170-172","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/countries.html","id":null,"dir":"Reference","previous_headings":"","what":"Countries — countries","title":"Countries — countries","text":"Student ratings 13 countries six attributes.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/countries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Countries — countries","text":"","code":"countries"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/countries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Countries — countries","text":"data.frame 6 columns (attributes) 13 rows (countries).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/countries.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Countries — countries","text":"Greenacre, M. J. Biplots Practice. Bilbao: Fundación BBVA, 2010.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimensio-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in dimensio — dimensio-deprecated","title":"Deprecated Functions in dimensio — dimensio-deprecated","text":"functions still work removed (defunct) next version.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimensio-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions in dimensio — dimensio-deprecated","text":"","code":"stat_hull(   mapping = NULL,   data = NULL,   geom = \"polygon\",   position = \"identity\",   na.rm = FALSE,   show.legend = NA,   inherit.aes = TRUE,   ... )  plot_rows(object, ...)  # S4 method for MultivariateAnalysis plot_rows(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_columns(object, ...)  # S4 method for MultivariateAnalysis plot_columns(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  # S4 method for BootstrapPCA plot_columns(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_individuals(object, ...)  # S4 method for PCA plot_individuals(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_variables(object, ...)  # S4 method for PCA plot_variables(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   linetype = NULL,   size = NULL,   group = NULL )  plot_variance(object, ...)  # S4 method for MultivariateAnalysis plot_variance(   object,   select = NULL,   variance = TRUE,   cumulative = TRUE,   fill = \"grey30\",   border = \"grey10\",   colour = \"red\" )  plot_contributions(object, ...)  # S4 method for MultivariateAnalysis plot_contributions(   object,   margin = 2,   axes = 1,   sort = TRUE,   decreasing = TRUE,   limit = 10,   fill = \"grey30\",   border = \"grey10\" )  plot_cos2(object, ...)  # S4 method for MultivariateAnalysis plot_cos2(   object,   margin = 2,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   sort = TRUE,   decreasing = TRUE,   limit = 10,   fill = \"grey30\",   border = \"grey10\" )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimensio-package.html","id":null,"dir":"Reference","previous_headings":"","what":"dimensio: Multivariate Data Analysis — dimensio-package","title":"dimensio: Multivariate Data Analysis — dimensio-package","text":"Simple Principal Components Analysis (PCA) Correspondence Analysis (CA) based Singular Value Decomposition (SVD). package provides S4 classes methods compute, extract, summarize visualize results multivariate data analysis. also includes methods partial bootstrap validation described Greenacre (1984) <isbn:978-0-12-299050-2> Lebart et al. (2006) <isbn:978-2-10-049616-7>.","code":""},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimensio-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"dimensio: Multivariate Data Analysis — dimensio-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 F-33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimnames.html","id":null,"dir":"Reference","previous_headings":"","what":"Dimnames of an Object — dimnames","title":"Dimnames of an Object — dimnames","text":"Retrieve set dimnames object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimnames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dimnames of an Object — dimnames","text":"","code":"# S4 method for MultivariateAnalysis dim(x)  # S4 method for MultivariateAnalysis rownames(x, do.NULL = TRUE, prefix = \"row\")  # S4 method for MultivariateAnalysis colnames(x, do.NULL = TRUE, prefix = \"col\")  # S4 method for MultivariateAnalysis dimnames(x)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimnames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dimnames of an Object — dimnames","text":"x object retrieve row column names (CA PCA object). .NULL logical scalar. FALSE names NULL, names created. prefix character string specifying prefix created names.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/dimnames.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dimnames of an Object — dimnames","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Contributions — get_contributions","title":"Get Contributions — get_contributions","text":"Get Contributions","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Contributions — get_contributions","text":"","code":"get_contributions(x, ...)  get_correlations(x, ...)  get_cos2(x, ...)  # S4 method for MultivariateAnalysis get_contributions(x, margin = 1)  # S4 method for PCA get_correlations(x, sup_name = \".sup\")  # S4 method for MultivariateAnalysis get_cos2(x, margin = 1, sup_name = \".sup\")"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Contributions — get_contributions","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. sup_name character string specifying name column create supplementary points attribution (see ).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Contributions — get_contributions","text":"get_contributions() returns data.frame contributions definition principal dimensions. get_correlations() returns data.frame correlations variables dimensions (PCA). extra column (named sup_name) added specifying whether observation supplementary point . get_cos2() returns data.frame \\(cos^2\\) values (.e. quality representation points factor map). extra column (named sup_name) added specifying whether observation supplementary point .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_contributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Contributions — get_contributions","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Coordinates — get_coordinates","title":"Get Coordinates — get_coordinates","text":"Get Coordinates","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Coordinates — get_coordinates","text":"","code":"get_coordinates(x, ...)  get_replications(x, ...)  # S4 method for MultivariateAnalysis get_coordinates(x, margin = 1, principal = TRUE, sup_name = \".sup\")  # S4 method for MultivariateBootstrap get_replications(x, margin = 1)  # S4 method for BootstrapPCA get_replications(x)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Coordinates — get_coordinates","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. principal logical scalar: principal coordinates returned? FALSE, standard coordinates returned. sup_name character string specifying name column create supplementary points attribution (see ).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Coordinates — get_coordinates","text":"get_coordinates() returns data.frame coordinates. extra column (named sup_name) added specifying whether observation supplementary point . get_replications() returns array coordinates.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Coordinates — get_coordinates","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_coordinates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Coordinates — get_coordinates","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 5:10) #> 1 qualitative variable was removed: Species.  ## Get row principal coordinates head(get_coordinates(X, margin = 1, principal = TRUE)) #>           F1         F2          F3  .sup #> 1  -2.385846  0.5084067 -0.12383101 FALSE #> 2  -2.215654 -0.6515155 -0.23313750 FALSE #> 3  -2.496566 -0.3169280  0.04974232 FALSE #> 4  -2.433607 -0.5744432  0.09690301 FALSE #> 11 -2.279483  1.0742447 -0.26629760 FALSE #> 12 -2.450871  0.1591839  0.09988982 FALSE  ## Get row standard coordinates head(get_coordinates(X, margin = 1, principal = FALSE)) #>          F1         F2         F3  .sup #> 1 -1.403075  0.5268995 -0.3142569 FALSE #> 2 -1.302987 -0.6752138 -0.5916536 FALSE #> 3 -1.468187 -0.3284560  0.1262355 FALSE #> 4 -1.431162 -0.5953380  0.2459193 FALSE #> 5 -1.340525  1.1133193 -0.6758069 FALSE #> 6 -1.441315  0.1649741  0.2534992 FALSE  ## Tidy principal coordinates head(tidy(X, margin = 1)) #>   label component supplementary coordinate contribution        cos2 #> 1     1        F1         FALSE -2.3858463   1.36709655 0.954033073 #> 2     1        F2         FALSE  0.5084067   0.19279380 0.043321256 #> 3     1        F3         FALSE -0.1238310   0.06858151 0.002570025 #> 4    10        F1          TRUE -2.3173283           NA 0.953213276 #> 5    10        F2          TRUE -0.4448514           NA 0.035127301 #> 6    10        F3          TRUE -0.2525218           NA 0.011319124 head(tidy(X, margin = 2)) #>          label component supplementary coordinate contribution         cos2 #> 1 Petal.Length        F1         FALSE 0.99121597  33.97921607 0.9825091061 #> 2 Petal.Length        F2         FALSE 0.01655680   0.02944324 0.0002741275 #> 3 Petal.Length        F3         FALSE 0.05477927   1.93260521 0.0030007681 #> 4  Petal.Width        F1         FALSE 0.96350955  32.10619376 0.9283506618 #> 5  Petal.Width        F2         FALSE 0.05756108   0.35586957 0.0033132775 #> 6  Petal.Width        F3         FALSE 0.24948796  40.08758618 0.0622442430  head(augment(X, margin = 1, axes = c(1, 2))) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.385846  0.5084067     1         FALSE 0.006944444 5.950740     4.132458 #> 2 -2.215654 -0.6515155     2         FALSE 0.006944444 5.333593     3.703884 #> 3 -2.496566 -0.3169280     3         FALSE 0.006944444 6.333284     4.398114 #> 4 -2.433607 -0.5744432     4         FALSE 0.006944444 6.252426     4.341962 #> 5 -2.279483  1.0742447    11         FALSE          NA 6.350046     4.409754 #> 6 -2.450871  0.1591839    12         FALSE          NA 6.032107     4.188963 #>        cos2 #> 1 0.9973543 #> 2 0.9880287 #> 3 0.9995093 #> 4 0.9977072 #> 5 0.9889258 #> 6 0.9951651 head(augment(X, margin = 2, axes = c(1, 2))) #>           F1         F2        label supplementary mass       sum contribution #> 1  0.8875399 0.36087785 Sepal.Length         FALSE    1 0.9179599     91.79599 #> 2 -0.4392190 0.89286998  Sepal.Width         FALSE    1 0.9901302     99.01302 #> 3  0.9912160 0.01655680 Petal.Length         FALSE    1 0.9827832     98.27832 #> 4  0.9635096 0.05756108  Petal.Width         FALSE    1 0.9316639     93.16639 #>        cos2 #> 1 0.9179599 #> 2 0.9901302 #> 3 0.9827832 #> 4 0.9316639"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Original Data — get_data","title":"Get Original Data — get_data","text":"Get Original Data","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Original Data — get_data","text":"","code":"get_data(x, ...)  # S4 method for MultivariateAnalysis get_data(x)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Original Data — get_data","text":"x object get element(s) (CA PCA object). ... Currently used.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Original Data — get_data","text":"Returns data.frame original data.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Original Data — get_data","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Distances — get_distances","title":"Get Distances — get_distances","text":"Get Distances","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Distances — get_distances","text":"","code":"get_distances(x, ...)  # S4 method for MultivariateAnalysis get_distances(x, margin = 1)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Distances — get_distances","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Distances — get_distances","text":"get_distances() returns numeric vector squared distance centroide.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_distances.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Distances — get_distances","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Eigenvalues — get_eigenvalues","title":"Get Eigenvalues — get_eigenvalues","text":"Get Eigenvalues","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Eigenvalues — get_eigenvalues","text":"","code":"get_eigenvalues(x)  get_inertia(x, ...)  get_variance(x, ...)  # S4 method for MultivariateAnalysis get_eigenvalues(x)  # S4 method for MultivariateAnalysis get_inertia(x, margin = 1)  # S4 method for MultivariateAnalysis get_variance(x, digits = 2)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Eigenvalues — get_eigenvalues","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. digits integer indicating number decimal places used.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Eigenvalues — get_eigenvalues","text":"get_eigenvalues() returns data.frame following columns: eigenvalues, variance (percentage variance) cumulative (cumulative percentage variance). get_variance() returns numeric vector giving percentage explained variance dimension. get_inertia() returns numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/get_eigenvalues.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Eigenvalues — get_eigenvalues","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Loadings — loadings","title":"Extract Loadings — loadings","text":"Extract loadingsin principal components analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Loadings — loadings","text":"","code":"# S4 method for PCA loadings(x)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Loadings — loadings","text":"x PCA object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Loadings — loadings","text":"Returns variable loadings (.e. coefficients linear combination original variables).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Extract Loadings — loadings","text":"loadings() implemented consistency [stats][stats::loadings].","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/loadings.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Loadings — loadings","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis — pca","title":"Principal Components Analysis — pca","text":"Computes principal components analysis based singular value decomposition.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis — pca","text":"","code":"pca(object, ...)  # S4 method for data.frame pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for matrix pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis — pca","text":"object \\(m \\times p\\) numeric matrix data.frame. ... Currently used. center logical scalar: variables shifted zero centered? scale logical scalar: variables scaled unit variance? rank integer value specifying maximal number components kept results. NULL (default), \\(p - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows (individuals). sup_col numeric logical vector specifying indices supplementary columns (variables). weight_row numeric vector specifying active row (individual) weights. NULL (default), uniform weights used. Row weights internally normalized sum 1 weight_col numeric vector specifying active column (variable) weights. NULL (default), uniform weights (1) used.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis — pca","text":"PCA object.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Principal Components Analysis — pca","text":"Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis — pca","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis — pca","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2) #> 1 qualitative variable was removed: Species.  ## Get row coordinates head(get_coordinates(X, margin = 1)) #>          F1          F2  .sup #> 1 -2.035960  0.31581917 FALSE #> 2 -2.169988  0.12171682 FALSE #> 3 -2.337513 -0.05789049 FALSE #> 4 -2.337532 -0.18393173 FALSE #> 5 -2.102974  0.21876799 FALSE #> 6 -1.582823  0.42642756 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                      F1         F2  .sup #> Sepal.Length  0.9345943  0.3545602 FALSE #> Petal.Length  0.9844440 -0.1115899 FALSE #> Petal.Width   0.9671282 -0.2290451 FALSE #> Sepal.Width  -0.3006341  0.4661255  TRUE  ## Get correlations between variables and dimensions head(get_correlations(X)) #>                      F1         F2  .sup #> Sepal.Length  0.9345943  0.3545602 FALSE #> Petal.Length  0.9844440 -0.1115899 FALSE #> Petal.Width   0.9671282 -0.2290451 FALSE #> Sepal.Width  -0.3006341  0.4661255  TRUE  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.7779334 93.578472   93.57847 #> F2   0.1906269  6.421528  100.00000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict New Coordinates — predict","title":"Predict New Coordinates — predict","text":"Predict projection new individuals/rows variables/columns.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict New Coordinates — predict","text":"","code":"# S4 method for CA predict(object, newdata, margin = 1)  # S4 method for PCA predict(object, newdata, margin = 1)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict New Coordinates — predict","text":"object CA PCA object. newdata object supplementary points coercible matrix compute principal coordinates. margin length-one numeric vector giving subscript data predicted: 1 indicates individuals/rows (default), 2 indicates variables/columns.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict New Coordinates — predict","text":"data.frame coordinates.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict New Coordinates — predict","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict New Coordinates — predict","text":"","code":"## Create a matrix A <- matrix(data = sample(1:10, 100, TRUE), nrow = 10, ncol = 10)  ## Compute correspondence analysis X <- ca(A, sup_row = 8:10, sup_col = 7:10)  ## Predict new row coordinates Y <- matrix(data = sample(1:10, 120, TRUE), nrow = 20, ncol = 6) predict(X, Y, margin = 1) #>             F1          F2            F3           F4           F5 #> 1   0.17471703 -0.07345523 -0.2675224180 -0.016461985  0.131859566 #> 2   0.56372762  0.16957306 -0.1007627536 -0.045990757  0.411294332 #> 3  -0.22449955  0.09186010  0.0369589806 -0.104655765  0.002451712 #> 4   0.63896235  0.29815571 -0.0852431293  0.015201142 -0.451513855 #> 5  -0.01845217 -0.17855777 -0.0741434899  0.298677225  0.438098455 #> 6  -0.03479994 -0.17279067 -0.0776743260 -0.136296505  0.138266904 #> 7   0.47801479 -0.17460920  0.4123742862 -0.200470446 -0.121118638 #> 8  -0.08234559 -0.20504704  0.4427467232 -0.278209716  0.087579646 #> 9   0.21324015  0.03726549 -0.2104299068  0.240673400 -0.066782483 #> 10  0.24811728 -0.33282164  0.1602755166 -0.003335075 -0.215721088 #> 11  0.03208209  0.30410017 -0.2382955604  0.045746891  0.248711152 #> 12  0.27969423  0.08527592  0.3985796748  0.401317525  0.153434400 #> 13  0.07625653 -0.04916791  0.2464073168 -0.223718176  0.069195721 #> 14  0.40027196  0.01657354  0.2767866947 -0.316098142 -0.110760529 #> 15  0.25281186  0.06561033  0.1618805607  0.029036797  0.135733024 #> 16 -0.10415927 -0.04558003  0.0390609697  0.529081002 -0.047924800 #> 17 -0.11226220  0.04858745 -0.0002276991 -0.103555348 -0.169071476 #> 18  0.19181059 -0.03719919 -0.2290754735 -0.003189704 -0.020388346 #> 19  0.34062276  0.32012843  0.3501265861  0.630473546 -0.008598229 #> 20 -0.04408791  0.25860195 -0.2498443398  0.142807006  0.115622270  ## Predict new column coordinates Z <- matrix(data = sample(1:10, 140, TRUE), nrow = 7, ncol = 20) predict(X, Z, margin = 2) #>             F1           F2           F3           F4           F5 #> 1  -0.37468727  0.267544239  0.379431411  0.092072423 -0.063395536 #> 2  -0.35514173  0.201801672  0.187809125  0.081584050  0.276498726 #> 3   0.34075490  0.029827461  0.018081686 -0.081435548  0.069575617 #> 4   0.56008251 -0.059289422 -0.326660405 -0.085300260 -0.271063174 #> 5   0.18090639 -0.006662934 -0.004062043 -0.056018727 -0.103853822 #> 6  -0.17318505 -0.227625496  0.268051373  0.049581840  0.072724542 #> 7   0.18830472 -0.575673823  0.094792921 -0.190411571  0.030318070 #> 8  -0.49497642 -0.094899901 -0.187268910 -0.107039912  0.222990309 #> 9  -0.10671904 -0.074753812 -0.215441336  0.043540684 -0.180243373 #> 10  0.13819071  0.200383814  0.251182019  0.398210599  0.351916770 #> 11  0.18294267  0.100883980  0.100368169  0.140975766 -0.043048524 #> 12 -0.37689643  0.038547535 -0.026556652 -0.021784260 -0.372157907 #> 13 -0.26041527  0.105391410  0.332961308  0.027724387 -0.006379742 #> 14  0.15164241 -0.142508621 -0.180439248 -0.257154858 -0.120254237 #> 15 -0.01429101  0.304113288  0.021080097 -0.171892459 -0.289524670 #> 16 -0.41145741 -0.160076170 -0.470983963 -0.105484715  0.292064565 #> 17 -0.19878412 -0.046358033  0.068842960 -0.003079981  0.159552838 #> 18 -0.25334874  0.078506894  0.270100053 -0.102226801 -0.400739267 #> 19 -0.09957540 -0.080938591  0.202546040 -0.087392983  0.125346702 #> 20 -0.32567757 -0.220010893  0.153607703 -0.103977758 -0.235926062"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Scree Plot — screeplot","title":"Scree Plot — screeplot","text":"Plot eigenvalues (scree plot) variances histogram.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scree Plot — screeplot","text":"","code":"# S4 method for MultivariateAnalysis screeplot(   x,   eigenvalues = FALSE,   cumulative = FALSE,   labels = TRUE,   limit = 10,   col = \"grey90\",   border = \"grey10\",   col.cumulative = \"red\",   lty.cumulative = \"solid\",   lwd.cumulative = 2,   ... )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scree Plot — screeplot","text":"x CA PCA object. eigenvalues logical scalar: eigenvalues plotted instead variance/inertia? cumulative logical scalar: cumulative percentages variance plotted? labels logical scalar: text labels drawn top bars? limit integer specifying number top elements displayed. col, border character string specifying bars infilling border colors. col.cumulative specification line color. lty.cumulative specification line type. lwd.cumulative specification line width. ... Extra parameters passed graphics::barplot().","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scree Plot — screeplot","text":"screeplot() called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scree Plot — screeplot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/screeplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scree Plot — screeplot","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Screeplot screeplot(X)  screeplot(X, cumulative = TRUE)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Parts of an Object — subset","title":"Extract Parts of an Object — subset","text":"Operators acting objects extract parts.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Parts of an Object — subset","text":"","code":"# S4 method for CA,ANY,missing [[(x, i)  # S4 method for PCA,ANY,missing [[(x, i)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Parts of an Object — subset","text":"x object extract element(s) replace element(s). character string specifying elements extract. unambiguous substring can given (see details).","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Parts of an Object — subset","text":"list.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Parts of an Object — subset","text":"\"data\", returns list following elements: data numeric matrix raw data. mean numeric vector giving variables means (PCA). sd numeric vector giving variables standard deviations (PCA). \"rows\", returns list following elements: coord numeric matrix rows/individuals coordinates. cos2 numeric matrix rows/individuals squared cosine. masses numeric vector giving rows masses/individual weights. sup logical vector specifying whether point supplementary observation . \"columns\", returns list following elements: coord numeric matrix columns/variables coordinates. cor numeric matrix correlation variables dimensions (PCA). cos2 numeric matrix columns/variables squared cosine. masses numeric vector giving columns masses/variable weights. sup logical vector specifying whether point supplementary observation . \"eigenvalues\", returns numeric vector eigenvalues.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Parts of an Object — subset","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Parts of an Object — subset","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 8:10, sup_col = 1) #> 1 qualitative variable was removed: Species.  ## Get results for the individuals X[[\"rows\"]] #> $coordinates #>             F1           F2 #> 1   -2.1799607  0.107860619 #> 2   -1.6996139 -0.926588874 #> 3   -1.9288393 -0.527485410 #> 4   -1.7585967 -0.705022642 #> 5   -2.2760301  0.314750517 #> 6   -2.2841520  1.069266219 #> 7   -1.9994783 -0.054120776 #> 11  -2.3350128  0.536316749 #> 12  -2.0097181 -0.069676614 #> 13  -1.7840271 -0.971497378 #> 14  -1.8952870 -1.015526377 #> 15  -2.7344808  1.112957446 #> 16  -2.8386721  2.074363046 #> 17  -2.4324986  1.010560888 #> 18  -2.0955476  0.152769123 #> 19  -2.2724958  0.817467817 #> 20  -2.3466691  0.788115151 #> 21  -1.9726315 -0.055000281 #> 22  -2.1661866  0.626133757 #> 23  -2.4243767  0.256045186 #> 24  -1.6233228 -0.127164668 #> 25  -1.8984582 -0.025647615 #> 26  -1.6254407 -0.897236208 #> 27  -1.8408919  0.020140394 #> 28  -2.1428741  0.122536952 #> 29  -2.0838914 -0.099029280 #> 30  -1.8175794 -0.483456411 #> 31  -1.7215100 -0.690346310 #> 32  -1.8779785  0.005464061 #> 33  -2.8037034  1.318967839 #> 34  -2.8524463  1.556089908 #> 35  -1.7585967 -0.705022642 #> 36  -1.9659259 -0.542161743 #> 37  -2.2170474  0.093184286 #> 38  -2.3604432  0.269842013 #> 39  -1.7367006 -0.941265207 #> 40  -2.0468047 -0.084352947 #> 41  -2.1326343  0.138092790 #> 42  -0.9798020 -2.344585993 #> 43  -1.9288393 -0.527485410 #> 44  -1.7681350  0.316847300 #> 45  -2.1139094  0.891728987 #> 46  -1.6152008 -0.881680370 #> 47  -2.3939955  0.757882980 #> 48  -1.8917527 -0.512809077 #> 49  -2.3350128  0.536316749 #> 50  -1.9878220 -0.305919178 #> 51   0.3450638  0.510411957 #> 52   0.3553036  0.525967795 #> 53   0.5997195  0.377783228 #> 54   0.8656684 -1.499239964 #> 55   0.7766677 -0.286915466 #> 56   0.5707548 -0.391408807 #> 57   0.4178206  0.807118863 #> 58   0.2567533 -1.529809908 #> 59   0.5117721 -0.169842576 #> 60   0.5287175 -0.641448199 #> 61   0.7152040 -2.328016836 #> 62   0.4361824  0.068158999 #> 63   0.7084985 -1.840855375 #> 64   0.6332719 -0.110257739 #> 65   0.1409057 -0.316605905 #> 66   0.3298732  0.259493060 #> 67   0.5474423  0.112187998 #> 68   0.2652383 -0.791729549 #> 69   1.3159972 -1.542931190 #> 70   0.4676168 -1.189953508 #> 71   0.7198029  0.704722305 #> 72   0.3853216 -0.464790472 #> 73   1.1761357 -0.863556163 #> 74   0.5605150 -0.406964645 #> 75   0.4005122 -0.213871574 #> 76   0.4259426  0.052603161 #> 77   0.7664279 -0.302471304 #> 78   0.9017017  0.275386670 #> 79   0.6435117 -0.094701901 #> 80   0.1387878 -1.086677445 #> 81   0.5265996 -1.411519740 #> 82   0.4050998 -1.471104576 #> 83   0.3598912 -0.731265207 #> 84   1.1425834 -0.375515196 #> 85   0.5474423  0.112187998 #> 86   0.2475780  0.984656096 #> 87   0.5255462  0.348430562 #> 88   1.0140150 -1.440534633 #> 89   0.2302695 -0.036334342 #> 90   0.6735297 -1.085460167 #> 91   0.6413938 -0.864773441 #> 92   0.5001159  0.081955827 #> 93   0.4930472 -0.923478773 #> 94   0.3528226 -1.736699807 #> 95   0.5555643 -0.642327704 #> 96   0.1829431 -0.066566513 #> 97   0.3634255 -0.228547907 #> 98   0.4005122 -0.213871574 #> 99   0.1338371 -1.322040504 #> 100  0.4224083 -0.450114139 #> 101  1.6596649  1.402087727 #> 102  1.3958227 -0.240789684 #> 103  1.5731339  0.587107682 #> 104  1.3047041  0.201463273 #> 105  1.6204604  0.617339853 #> 106  1.8327404  0.689842013 #> 107  1.1966153 -0.832444487 #> 108  1.5643105  0.304197604 #> 109  1.7631548 -0.596743655 #> 110  1.4085435  2.037433755 #> 111  0.9998890  0.838568312 #> 112  1.4699960 -0.211437019 #> 113  1.4247874  0.528402351 #> 114  1.6352879 -0.624337311 #> 115  1.7218189  0.190642734 #> 116  1.3273016  1.002646490 #> 117  1.1715481  0.393676839 #> 118  1.1856853  2.404546038 #> 119  2.4971040 -0.003871575 #> 120  1.5014304 -1.469549526 #> 121  1.4756482  1.061351821 #> 122  1.3099931 -0.018343948 #> 123  1.9775527  0.245830045 #> 124  1.2372363 -0.315050854 #> 125  1.2107526  1.178424712 #> 126  1.1648425  0.880838301 #> 127  1.1040803 -0.122837289 #> 128  0.9490282  0.305618841 #> 129  1.6540127  0.129298887 #> 130  1.1139818  0.347888830 #> 131  1.6706197  0.112863543 #> 132  0.9055992  2.270700031 #> 133  1.7384258  0.174207390 #> 134  0.9621009 -0.213533802 #> 135  1.2552597 -0.598840438 #> 136  1.8161334  0.706277356 #> 137  1.3308359  1.505363789 #> 138  1.0754787  0.600566737 #> 139  0.9119416  0.290942508 #> 140  1.2916314  0.720615916 #> 141  1.6190440  0.884694094 #> 142  1.3491977  0.766403925 #> 143  1.3958227 -0.240789684 #> 144  1.5498214  1.090704487 #> 145  1.5484050  1.358058728 #> 146  1.4823537  0.574190360 #> 147  1.5508748 -0.669245814 #> 148  1.2291144  0.439464848 #> 149  1.1722495  1.431102620 #> 150  1.0232015  0.334971507 #> 8   -2.0468047 -0.084352947 #> 9   -1.6035446 -1.133478772 #> 10  -1.8430098 -0.749931146 #>  #> $contributions #>              F1           F2 #> 1   1.453343684 0.0106993487 #> 2   0.883428800 0.7895973615 #> 3   1.137792525 0.2558894786 #> 4   0.945809064 0.4571278240 #> 5   1.584261926 0.0911096611 #> 6   1.595588876 1.0514853344 #> 7   1.222656219 0.0026937641 #> 11  1.667437334 0.2645295822 #> 12  1.235211341 0.0044648369 #> 13  0.973360803 0.8679901399 #> 14  1.098552806 0.9484489111 #> 15  2.286760836 1.1391702764 #> 16  2.464344658 3.9573190026 #> 17  1.809573309 0.9391965689 #> 18  1.342969212 0.0214636064 #> 19  1.579345535 0.6145721192 #> 20  1.684126390 0.5712298316 #> 21  1.190043653 0.0027820270 #> 22  1.435035728 0.3605502185 #> 23  1.797509409 0.0602927030 #> 24  0.805899240 0.0148718312 #> 25  1.102232091 0.0006049584 #> 26  0.808003463 0.7403636858 #> 27  1.036400344 0.0003730503 #> 28  1.404314222 0.0138091094 #> 29  1.328070529 0.0090190079 #> 30  1.010317225 0.2149543180 #> 31  0.906337800 0.4382940161 #> 32  1.078579659 0.0000274576 #> 33  2.404003572 1.5999261560 #> 34  2.488318246 2.2269009410 #> 35  0.945809064 0.4571278240 #> 36  1.181966834 0.2703268996 #> 37  1.503214416 0.0079857717 #> 38  1.703954895 0.0669654302 #> 39  0.922403375 0.8148084750 #> 40  1.281220300 0.0065438305 #> 41  1.390925123 0.0175377362 #> 42  0.293594279 5.0554970160 #> 43  1.137792525 0.2558894786 #> 44  0.956096741 0.0923276004 #> 45  1.366607256 0.7313028984 #> 46  0.797855133 0.7149141115 #> 47  1.752740637 0.5282455348 #> 48  1.094459487 0.2418482414 #> 49  1.667437334 0.2645295822 #> 50  1.208442464 0.0860686388 #> 51  0.036414113 0.2395924925 #> 52  0.038607369 0.2544191706 #> 53  0.109993587 0.1312553072 #> 54  0.229178638 2.0671587361 #> 55  0.184476671 0.0757075942 #> 56  0.099625421 0.1408940695 #> 57  0.053388873 0.5991099343 #> 58  0.020160574 2.1523181354 #> 59  0.080098468 0.0265292309 #> 60  0.085490582 0.3784031185 #> 61  0.156433881 4.9842952461 #> 62  0.058184494 0.0042724593 #> 63  0.153514287 3.1165268007 #> 64  0.122645427 0.0111802030 #> 65  0.006071949 0.0921869714 #> 66  0.033278603 0.0619274246 #> 67  0.091653220 0.0115750885 #> 68  0.021515104 0.5764813136 #> 69  0.529640167 2.1893976274 #> 70  0.066873073 1.3022414468 #> 71  0.158452140 0.4567384375 #> 72  0.045406471 0.1986762522 #> 73  0.423044289 0.6858240370 #> 74  0.096082758 0.1523157768 #> 75  0.049057166 0.0420666239 #> 76  0.055484680 0.0025448091 #> 77  0.179644343 0.0841394918 #> 78  0.248654774 0.0697456870 #> 79  0.126643778 0.0082480050 #> 80  0.005890792 1.0860075196 #> 81  0.084807053 1.8323373666 #> 82  0.050187444 1.9903003497 #> 83  0.039610792 0.4917918728 #> 84  0.399251696 0.1296840484 #> 85  0.091653220 0.0115750885 #> 86  0.018745415 0.8916628530 #> 87  0.084468126 0.1116513580 #> 88  0.314455886 1.9084418605 #> 89  0.016216010 0.0012141326 #> 90  0.138734505 1.0835758275 #> 91  0.125811545 0.6877588893 #> 92  0.076491334 0.0061771934 #> 93  0.074344362 0.7843056805 #> 94  0.038070081 2.7738376469 #> 95  0.094392957 0.3794415052 #> 96  0.010235356 0.0040751457 #> 97  0.040392607 0.0480381224 #> 98  0.049057166 0.0420666239 #> 99  0.005478023 1.6073892100 #> 100 0.054567719 0.1863274482 #> 101 0.842387320 1.8079312085 #> 102 0.595842510 0.0533221045 #> 103 0.756837054 0.3170056865 #> 104 0.520589025 0.0373270182 #> 105 0.803059702 0.3504936509 #> 106 1.027242319 0.4376539041 #> 107 0.437905211 0.6372973156 #> 108 0.748370960 0.0851026540 #> 109 0.950718311 0.3274968644 #> 110 0.606752411 3.8176711884 #> 111 0.305755696 0.6467082790 #> 112 0.660850565 0.0411143642 #> 113 0.620827724 0.2567798899 #> 114 0.817823172 0.3584842707 #> 115 0.906663024 0.0334250480 #> 116 0.538778450 0.9245431850 #> 117 0.419750477 0.1425316314 #> 118 0.429941982 5.3173802664 #> 119 1.906971687 0.0000137850 #> 120 0.689416059 1.9860948268 #> 121 0.665942333 1.0359773621 #> 122 0.524818373 0.0003094690 #> 123 1.195988781 0.0555778122 #> 124 0.468140574 0.0912836189 #> 125 0.448313474 1.2771303006 #> 126 0.414959229 0.7135491729 #> 127 0.372796904 0.0138768844 #> 128 0.275441407 0.0858997255 #> 129 0.836659379 0.0153752114 #> 130 0.379513426 0.1113044416 #> 131 0.853544550 0.0117149082 #> 132 0.250808937 4.7418854471 #> 133 0.924236944 0.0279103111 #> 134 0.283081973 0.0419338551 #> 135 0.481879173 0.3298023644 #> 136 1.008710388 0.4587563529 #> 137 0.541651566 2.0840803395 #> 138 0.353732210 0.3317065721 #> 139 0.254334345 0.0778477176 #> 140 0.510209050 0.4775724073 #> 141 0.801656426 0.7198098450 #> 142 0.556701191 0.5401905310 #> 143 0.595842510 0.0533221045 #> 144 0.734571902 1.0940715530 #> 145 0.733229824 1.6961670731 #> 146 0.672008322 0.3032098599 #> 147 0.735570728 0.4119104916 #> 148 0.462014453 0.1776150699 #> 149 0.420253277 1.8835323329 #> 150 0.320179346 0.1031922926 #>  #> $cos2 #>              F1           F2 #> 1   0.997537883 2.442069e-03 #> 2   0.770275213 2.289389e-01 #> 3   0.929285621 6.949892e-02 #> 4   0.861533164 1.384668e-01 #> 5   0.981140144 1.876324e-02 #> 6   0.820250332 1.797497e-01 #> 7   0.997006440 7.304536e-04 #> 11  0.948921305 5.006041e-02 #> 12  0.997254638 1.198698e-03 #> 13  0.771015900 2.286355e-01 #> 14  0.775718588 2.227084e-01 #> 15  0.857879117 1.421129e-01 #> 16  0.651874030 3.480990e-01 #> 17  0.849498051 1.466163e-01 #> 18  0.993184069 5.278443e-03 #> 19  0.884472859 1.144510e-01 #> 20  0.898639841 1.013588e-01 #> 21  0.995527356 7.739112e-04 #> 22  0.920782450 7.693067e-02 #> 23  0.985634960 1.099383e-02 #> 24  0.983278582 6.033923e-03 #> 25  0.988580295 1.804280e-04 #> 26  0.766287189 2.334871e-01 #> 27  0.996619952 1.192913e-04 #> 28  0.996181238 3.257461e-03 #> 29  0.997744694 2.253183e-03 #> 30  0.933168940 6.602192e-02 #> 31  0.861052131 1.384664e-01 #> 32  0.993951874 8.414246e-06 #> 33  0.814686002 1.802995e-01 #> 34  0.769931500 2.291324e-01 #> 35  0.861533164 1.384668e-01 #> 36  0.926583440 7.047060e-02 #> 37  0.998037699 1.763122e-03 #> 38  0.984800233 1.287005e-02 #> 39  0.771150422 2.265235e-01 #> 40  0.997960094 1.694966e-03 #> 41  0.992507874 4.161436e-03 #> 42  0.146925900 8.413062e-01 #> 43  0.929285621 6.949892e-02 #> 44  0.946393484 3.039071e-02 #> 45  0.848161656 1.509286e-01 #> 46  0.765582021 2.281188e-01 #> 47  0.906464208 9.084652e-02 #> 48  0.931348538 6.843763e-02 #> 49  0.948921305 5.006041e-02 #> 50  0.976803096 2.313478e-02 #> 51  0.277995466 6.082479e-01 #> 52  0.311683132 6.830184e-01 #> 53  0.664146963 2.635440e-01 #> 54  0.249361414 7.479428e-01 #> 55  0.878241702 1.198537e-01 #> 56  0.637260992 2.996949e-01 #> 57  0.210729871 7.863598e-01 #> 58  0.027308118 9.694709e-01 #> 59  0.758623302 8.355378e-02 #> 60  0.387926000 5.709851e-01 #> 61  0.086194377 9.132535e-01 #> 62  0.926162213 2.261503e-02 #> 63  0.128022840 8.642691e-01 #> 64  0.898327863 2.723157e-02 #> 65  0.130964651 6.612032e-01 #> 66  0.593595505 3.673229e-01 #> 67  0.958380776 4.024891e-02 #> 68  0.090772461 8.087892e-01 #> 69  0.420435646 5.779399e-01 #> 70  0.133260226 8.629393e-01 #> 71  0.504882110 4.839482e-01 #> 72  0.406578554 5.915782e-01 #> 73  0.645293645 3.478754e-01 #> 74  0.519247169 2.737241e-01 #> 75  0.734870489 2.095489e-01 #> 76  0.958138539 1.461335e-02 #> 77  0.812191218 1.264981e-01 #> 78  0.912896321 8.514938e-02 #> 79  0.978657862 2.119510e-02 #> 80  0.016044824 9.836341e-01 #> 81  0.122144323 8.775790e-01 #> 82  0.070309187 9.272035e-01 #> 83  0.194938448 8.048312e-01 #> 84  0.891022209 9.624269e-02 #> 85  0.958380776 4.024891e-02 #> 86  0.059437245 9.401643e-01 #> 87  0.672034771 2.953945e-01 #> 88  0.330736771 6.674844e-01 #> 89  0.946744678 2.357189e-02 #> 90  0.277285564 7.201808e-01 #> 91  0.342463747 6.225436e-01 #> 92  0.891508689 2.394109e-02 #> 93  0.221487022 7.770072e-01 #> 94  0.039508517 9.572545e-01 #> 95  0.426831711 5.705601e-01 #> 96  0.490691231 6.496627e-02 #> 97  0.698527791 2.762532e-01 #> 98  0.734870489 2.095489e-01 #> 99  0.009680752 9.445952e-01 #> 100 0.467996371 5.314018e-01 #> 101 0.576108690 4.111629e-01 #> 102 0.961696898 2.861893e-02 #> 103 0.877177165 1.221776e-01 #> 104 0.958249977 2.284791e-02 #> 105 0.870879776 1.263950e-01 #> 106 0.851672810 1.206619e-01 #> 107 0.657810722 3.183482e-01 #> 108 0.886186912 3.351128e-02 #> 109 0.885499558 1.014339e-01 #> 110 0.321916434 6.735496e-01 #> 111 0.577480507 4.061726e-01 #> 112 0.978244776 2.023845e-02 #> 113 0.873487112 1.201395e-01 #> 114 0.848034409 1.236129e-01 #> 115 0.885718143 1.085828e-02 #> 116 0.607788840 3.468240e-01 #> 117 0.884632536 9.989009e-02 #> 118 0.191705611 7.884282e-01 #> 119 0.992440547 2.385649e-06 #> 120 0.508966080 4.875811e-01 #> 121 0.651305390 3.369285e-01 #> 122 0.949464886 1.861772e-04 #> 123 0.939398567 1.451656e-02 #> 124 0.929464957 6.026831e-02 #> 125 0.513528277 4.864713e-01 #> 126 0.594651369 3.400324e-01 #> 127 0.968111697 1.198353e-02 #> 128 0.898455967 9.317487e-02 #> 129 0.989867285 6.049073e-03 #> 130 0.787377903 7.679059e-02 #> 131 0.967682818 4.416571e-03 #> 132 0.132869218 8.353566e-01 #> 133 0.977445867 9.815525e-03 #> 134 0.899008289 4.428491e-02 #> 135 0.716813201 1.631402e-01 #> 136 0.867820065 1.312456e-01 #> 137 0.428750425 5.485781e-01 #> 138 0.748409494 2.333771e-01 #> 139 0.890507966 9.063963e-02 #> 140 0.754859896 2.349616e-01 #> 141 0.744129523 2.221864e-01 #> 142 0.695525700 2.244282e-01 #> 143 0.961696898 2.861893e-02 #> 144 0.666268263 3.299894e-01 #> 145 0.547542725 4.211977e-01 #> 146 0.810397292 1.215923e-01 #> 147 0.830568366 1.546654e-01 #> 148 0.874671814 1.118173e-01 #> 149 0.391519529 5.835188e-01 #> 150 0.903130625 9.679300e-02 #> 8   0.997960094 1.694966e-03 #> 9   0.666045138 3.327885e-01 #> 10  0.856123843 1.417503e-01 #>  #> $masses #>           1           2           3           4           5           6  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>           7          14          15          16          17          18  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          19          20          21          22          23          24  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          25          26          27          28          29          30  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          31          32          33          34          35          36  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          37          38          39          40          41          42  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          43          44          45          46          47          48  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          49          50          51          52          53          54  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          55          56          57          58          59          60  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          61          62          63          64          65          66  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          67          68          69          70          71          72  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          73          74          75          76          77          78  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          79          80          81          82          83          84  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          85          86          87          88          89          90  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          91          92          93          94          95          96  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          97          98          99         100         101         102  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         103         104         105         106         107         108  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         109         110         111         112         113         114  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         115         116         117         118         119         120  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         121         122         123         124         125         126  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         127         128         129         130         131         132  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         133         134         135         136         137         138  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         139         140         141         142         143         144  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         145         146         147         148         149         150  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>           8           9          10  #> 0.006802721 0.006802721 0.006802721  #>  #> $supplement #>   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [145] FALSE FALSE FALSE  TRUE  TRUE  TRUE #>"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Object Summaries — summary","title":"Object Summaries — summary","text":"Provides summary results multivariate data analysis.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Object Summaries — summary","text":"","code":"# S4 method for CA summary(object, margin = 1, active = TRUE, sup = TRUE, rank = 3)  # S4 method for PCA summary(object, margin = 1, active = TRUE, sup = TRUE, rank = 3)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Object Summaries — summary","text":"object CA PCA object. margin length-one numeric vector giving subscript data summarized: 1 indicates individuals/rows (default), 2 indicates variables/columns. active logical scalar: active observations summarized? sup logical scalar: supplementary observations summarized? rank integer value specifying maximal number components kept results.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Object Summaries — summary","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Object Summaries — summary","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## Compute correspondence analysis X <- ca(colours)  ## Rows summary summary(X, margin = 1) #> --- Correspondence Analysis (CA) ------------------------------------------------ #>  #> Eigenvalues: #>    eigenvalues variance cumulative #> F1       0.209   89.373     89.373 #> F2       0.022    9.515     98.888 #> F3       0.003    1.112    100.000 #>  #> Active rows: #>          inertia CA1_coord CA1_contrib CA1_cos2 CA2_coord CA2_contrib CA2_cos2 #> marron    93.086    -0.492      43.116    0.967    -0.088      13.042    0.031 #> noisette  13.089    -0.213       3.401    0.542     0.167      19.804    0.336 #> vert      16.085     0.162       1.355    0.176     0.339      55.910    0.773 #> bleu     111.337     0.547      52.128    0.977    -0.083      11.244    0.022 #>          CA3_coord CA3_contrib CA3_cos2 #> marron       0.022       6.680    0.002 #> noisette    -0.101      61.086    0.121 #> vert         0.088      31.925    0.052 #> bleu        -0.005       0.310    0.000  ## Columns summary summary(X, margin = 2) #> --- Correspondence Analysis (CA) ------------------------------------------------ #>  #> Eigenvalues: #>    eigenvalues variance cumulative #> F1       0.209   89.373     89.373 #> F2       0.022    9.515     98.888 #> F3       0.003    1.112    100.000 #>  #> Active columns: #>         inertia CA1_coord CA1_contrib CA1_cos2 CA2_coord CA2_contrib CA2_cos2 #> brun     55.425    -0.505      22.246    0.838    -0.215      37.877    0.152 #> chatain  12.284    -0.148       5.086    0.864     0.033       2.319    0.042 #> roux     15.095    -0.130       0.964    0.133     0.320      55.131    0.812 #> blond   150.793     0.835      71.704    0.993    -0.070       4.673    0.007 #>         CA3_coord CA3_contrib CA3_cos2 #> brun        0.056      21.633    0.010 #> chatain    -0.049      44.284    0.094 #> roux        0.083      31.913    0.055 #> blond       0.016       2.171    0.000"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Coordinates — tidy","title":"Tidy Coordinates — tidy","text":"Tidy Coordinates","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Coordinates — tidy","text":"","code":"tidy(x, ...)  augment(x, ...)  # S4 method for MultivariateAnalysis augment(x, margin = 1, axes = c(1, 2), principal = TRUE, ...)  # S4 method for MultivariateAnalysis tidy(x, margin = 1, principal = TRUE, ...)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Coordinates — tidy","text":"x CA PCA object. ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. principal logical scalar: principal coordinates returned? FALSE, standard coordinates returned.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Coordinates — tidy","text":"tidy() returns long data.frame following columns: label Row/column names original data. component Component. supplementary Whether observation active supplementary. coordinate Coordinates. contribution Contributions definition components. cos2 \\(cos^2\\). augment() returns wide data.frame row/column coordinates along axes following columns: label Row/column names original data. supplementary Whether observation active supplementary. mass Weight/mass observation. sum Sum squared coordinates along axes. contribution Joint contributions definition axes. cos2 Joint \\(cos^2\\) along axes.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Coordinates — tidy","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/tidy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Coordinates — tidy","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 5:10) #> 1 qualitative variable was removed: Species.  ## Get row principal coordinates head(get_coordinates(X, margin = 1, principal = TRUE)) #>           F1         F2          F3  .sup #> 1  -2.385846  0.5084067 -0.12383101 FALSE #> 2  -2.215654 -0.6515155 -0.23313750 FALSE #> 3  -2.496566 -0.3169280  0.04974232 FALSE #> 4  -2.433607 -0.5744432  0.09690301 FALSE #> 11 -2.279483  1.0742447 -0.26629760 FALSE #> 12 -2.450871  0.1591839  0.09988982 FALSE  ## Get row standard coordinates head(get_coordinates(X, margin = 1, principal = FALSE)) #>          F1         F2         F3  .sup #> 1 -1.403075  0.5268995 -0.3142569 FALSE #> 2 -1.302987 -0.6752138 -0.5916536 FALSE #> 3 -1.468187 -0.3284560  0.1262355 FALSE #> 4 -1.431162 -0.5953380  0.2459193 FALSE #> 5 -1.340525  1.1133193 -0.6758069 FALSE #> 6 -1.441315  0.1649741  0.2534992 FALSE  ## Tidy principal coordinates head(tidy(X, margin = 1)) #>   label component supplementary coordinate contribution        cos2 #> 1     1        F1         FALSE -2.3858463   1.36709655 0.954033073 #> 2     1        F2         FALSE  0.5084067   0.19279380 0.043321256 #> 3     1        F3         FALSE -0.1238310   0.06858151 0.002570025 #> 4    10        F1          TRUE -2.3173283           NA 0.953213276 #> 5    10        F2          TRUE -0.4448514           NA 0.035127301 #> 6    10        F3          TRUE -0.2525218           NA 0.011319124 head(tidy(X, margin = 2)) #>          label component supplementary coordinate contribution         cos2 #> 1 Petal.Length        F1         FALSE 0.99121597  33.97921607 0.9825091061 #> 2 Petal.Length        F2         FALSE 0.01655680   0.02944324 0.0002741275 #> 3 Petal.Length        F3         FALSE 0.05477927   1.93260521 0.0030007681 #> 4  Petal.Width        F1         FALSE 0.96350955  32.10619376 0.9283506618 #> 5  Petal.Width        F2         FALSE 0.05756108   0.35586957 0.0033132775 #> 6  Petal.Width        F3         FALSE 0.24948796  40.08758618 0.0622442430  head(augment(X, margin = 1, axes = c(1, 2))) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.385846  0.5084067     1         FALSE 0.006944444 5.950740     4.132458 #> 2 -2.215654 -0.6515155     2         FALSE 0.006944444 5.333593     3.703884 #> 3 -2.496566 -0.3169280     3         FALSE 0.006944444 6.333284     4.398114 #> 4 -2.433607 -0.5744432     4         FALSE 0.006944444 6.252426     4.341962 #> 5 -2.279483  1.0742447    11         FALSE          NA 6.350046     4.409754 #> 6 -2.450871  0.1591839    12         FALSE          NA 6.032107     4.188963 #>        cos2 #> 1 0.9973543 #> 2 0.9880287 #> 3 0.9995093 #> 4 0.9977072 #> 5 0.9889258 #> 6 0.9951651 head(augment(X, margin = 2, axes = c(1, 2))) #>           F1         F2        label supplementary mass       sum contribution #> 1  0.8875399 0.36087785 Sepal.Length         FALSE    1 0.9179599     91.79599 #> 2 -0.4392190 0.89286998  Sepal.Width         FALSE    1 0.9901302     99.01302 #> 3  0.9912160 0.01655680 Petal.Length         FALSE    1 0.9827832     98.27832 #> 4  0.9635096 0.05756108  Petal.Width         FALSE    1 0.9316639     93.16639 #>        cos2 #> 1 0.9179599 #> 2 0.9901302 #> 3 0.9827832 #> 4 0.9316639"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Contributions and cos2 — viz_contributions","title":"Visualize Contributions and cos2 — viz_contributions","text":"Plots contributions histogram \\(cos^2\\) scatterplot.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Contributions and cos2 — viz_contributions","text":"","code":"viz_contributions(x, ...)  viz_cos2(x, ...)  # S4 method for MultivariateAnalysis viz_contributions(   x,   margin = 2,   axes = 1,   sort = TRUE,   decreasing = TRUE,   limit = 10,   horiz = FALSE,   col = \"grey90\",   border = \"grey10\",   ... )  # S4 method for MultivariateAnalysis viz_cos2(   x,   margin = 2,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   sort = TRUE,   decreasing = TRUE,   limit = 10,   horiz = FALSE,   col = \"grey90\",   border = \"grey10\",   ... )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Contributions and cos2 — viz_contributions","text":"x CA PCA object. ... Extra parameters passed graphics::barplot(). margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-one numeric vector giving dimensions plotted. sort logical scalar: data sorted? decreasing logical scalar: sort order decreasing? used sort TRUE. limit integer specifying number top elements displayed. horiz logical scalar: bars drawn horizontally first bottom? col, border character string specifying bars infilling border colors. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted?","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Contributions and cos2 — viz_contributions","text":"viz_contributions() viz_cos2() called side-effects: result graphic displayed. Invisibly return x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Contributions and cos2 — viz_contributions","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_contributions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Contributions and cos2 — viz_contributions","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Get row contributions head(get_contributions(X, margin = 1)) #>          F1         F2          F3 #> 1 1.1715796 0.16806554 0.074085470 #> 2 0.9891845 0.33146674 0.250034006 #> 3 1.2768164 0.08526419 0.008875320 #> 4 1.2077372 0.26029781 0.037858004 #> 5 1.3046313 0.30516562 0.001125175 #> 6 0.9841236 1.61748779 0.003303827  ## Plot contributions viz_contributions(X)   ## Plot cos2 viz_cos2(X)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Individuals Factor Map — viz_individuals","title":"Visualize Individuals Factor Map — viz_individuals","text":"Plots row/individual principal coordinates.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Individuals Factor Map — viz_individuals","text":"","code":"viz_individuals(x, ...)  viz_rows(x, ...)  # S4 method for MultivariateAnalysis viz_rows(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapCA viz_rows(x, axes = c(1, 2), ...)  # S4 method for PCA viz_individuals(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Individuals Factor Map — viz_individuals","text":"x CA PCA object. ... graphical parameters (see details). axes length-two numeric vector giving dimensions plotted. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels logical scalar: labels drawn? highlight vector specifying information highlighted. NULL (default), highlighting applied. mapped least one graphical parameters explicitly specified (see examples). single character string passed, must one \"observation\", \"mass\", \"sum\", \"contribution\" \"cos2\" (see augment()). unambiguous substring can given. xlim length-two numeric vector giving x limits plot. default value, NULL, indicates range finite values plotted used. ylim length-two numeric vector giving y limits plot. default value, NULL, indicates range finite values plotted used. main character string giving main title plot. sub character string giving subtitle 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/dimensio/dev/reference/viz_individuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Individuals Factor Map — viz_individuals","text":"viz_*() called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize Individuals Factor Map — viz_individuals","text":"Commonly used graphical parameters : pch vector plotting characters symbols. can either single character integer code one set graphics symbols. cex numerical vector giving amount plotting characters symbols scaled relative default. col colors lines points. Multiple colors can specified point can given color. bg background color open plot symbols given pch = 21:25.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize Individuals Factor Map — viz_individuals","text":"careful: graphical parameters silently recycled.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Individuals Factor Map — viz_individuals","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_individuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Individuals Factor Map — viz_individuals","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Plot individuals viz_individuals(X, panel.last = graphics::grid())   ## Plot variables viz_variables(X, panel.last = graphics::grid())   ## Graphical parameters ## Continuous values viz_individuals(X, highlight = iris$Petal.Length, pch = 16)  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"))  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"),                 cex = c(1, 2))   viz_variables(X, highlight = \"contribution\",               col = grDevices::hcl.colors(12, \"BluGrn\", rev = TRUE),               lwd = c(1, 5))   ## Discrete values viz_individuals(X, highlight = iris$Species, pch = 21:23)  viz_individuals(X, highlight = iris$Species, pch = 21:23,                 bg = c(\"#004488\", \"#DDAA33\", \"#BB5566\"),                 col = \"black\")   viz_variables(X, highlight = c(\"Petal\", \"Petal\", \"Sepal\", \"Sepal\"),               col = c(\"#EE7733\", \"#0077BB\"),               lty = c(1, 3))"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Variables Factor Map — viz_variables","title":"Visualize Variables Factor Map — viz_variables","text":"Plots column/variable principal coordinates.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Variables Factor Map — viz_variables","text":"","code":"viz_variables(x, ...)  viz_columns(x, ...)  # S4 method for MultivariateAnalysis viz_columns(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapCA viz_columns(x, axes = c(1, 2), ...)  # S4 method for PCA viz_variables(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = TRUE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for CA viz_variables(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   xlim = NULL,   ylim = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapPCA viz_variables(x, axes = c(1, 2), ...)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Variables Factor Map — viz_variables","text":"x CA PCA object. ... graphical parameters (see details). axes length-two numeric vector giving dimensions plotted. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels logical scalar: labels drawn? highlight vector specifying information highlighted. NULL (default), highlighting applied. mapped least one graphical parameters explicitly specified (see examples). single character string passed, must one \"observation\", \"mass\", \"sum\", \"contribution\" \"cos2\" (see augment()). unambiguous substring can given. xlim length-two numeric vector giving x limits plot. default value, NULL, indicates range finite values plotted used. ylim length-two numeric vector giving y limits plot. default value, NULL, indicates range finite values plotted used. main character string giving main title plot. sub character string giving subtitle 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/dimensio/dev/reference/viz_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Variables Factor Map — viz_variables","text":"viz_*() called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize Variables Factor Map — viz_variables","text":"Commonly used graphical parameters : pch vector plotting characters symbols. can either single character integer code one set graphics symbols. cex numerical vector giving amount plotting characters symbols scaled relative default. lty vector line types. lwd vector line widths. col colors lines points. Multiple colors can specified point can given color. bg background color open plot symbols given pch = 21:25.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize Variables Factor Map — viz_variables","text":"careful: graphical parameters silently recycled.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Variables Factor Map — viz_variables","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Variables Factor Map — viz_variables","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Plot individuals viz_individuals(X, panel.last = graphics::grid())   ## Plot variables viz_variables(X, panel.last = graphics::grid())   ## Graphical parameters ## Continuous values viz_individuals(X, highlight = iris$Petal.Length, pch = 16)  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"))  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"),                 cex = c(1, 2))   viz_variables(X, highlight = \"contribution\",               col = grDevices::hcl.colors(12, \"BluGrn\", rev = TRUE),               lwd = c(1, 5))   ## Discrete values viz_individuals(X, highlight = iris$Species, pch = 21:23)  viz_individuals(X, highlight = iris$Species, pch = 21:23,                 bg = c(\"#004488\", \"#DDAA33\", \"#BB5566\"),                 col = \"black\")   viz_variables(X, highlight = c(\"Petal\", \"Petal\", \"Sepal\", \"Sepal\"),               col = c(\"#EE7733\", \"#0077BB\"),               lty = c(1, 3))"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Envelopes — viz_wrap","title":"Plot Envelopes — viz_wrap","text":"Plot Envelopes","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Envelopes — viz_wrap","text":"","code":"viz_hull(x, ...)  viz_confidence(x, ...)  viz_tolerance(x, ...)  # S4 method for MultivariateAnalysis viz_tolerance(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95, ...)  # S4 method for BootstrapCA viz_tolerance(x, margin = 1, axes = c(1, 2), level = 0.95, ...)  # S4 method for MultivariateAnalysis viz_confidence(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95, ...)  # S4 method for BootstrapCA viz_confidence(x, margin = 1, axes = c(1, 2), level = 0.95, ...)  # S4 method for MultivariateAnalysis viz_hull(x, margin = 1, axes = c(1, 2), group = NULL, ...)  # S4 method for BootstrapCA viz_hull(x, margin = 1, axes = c(1, 2), ...)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Envelopes — viz_wrap","text":"x object wrap observations (CA PCA object). ... graphical parameters passed graphics::polygon(). margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. group vector specifying group observation belongs . level numeric vector specifying confidence/tolerance level.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Envelopes — viz_wrap","text":"viz_*()called side-effects: results graphic displayed. Invisibly returns x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Envelopes — viz_wrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/viz_wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Envelopes — viz_wrap","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Convex hull coordinates hulls <- wrap_hull(X, margin = 1, group = iris$Species)  ## Confidence ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species,                         level = c(0.68, 0.95))  ## Tolerance ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species, level = 0.95)  ## Plot with convex hulls col <- c(\"#004488\", \"#DDAA33\", \"#BB5566\") viz_rows(X, highlight = iris$Species, col = col) viz_hull(X, group = iris$Species, border = col)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrap Observations — wrap","title":"Wrap Observations — wrap","text":"wrap_hull() computes convex hull set observations. wrap_confidence() computes confidence ellipse. wrap_tolerance() computes tolerance ellipse.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrap Observations — wrap","text":"","code":"wrap_hull(x, ...)  wrap_confidence(x, ...)  wrap_tolerance(x, ...)  # S4 method for MultivariateAnalysis wrap_confidence(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95)  # S4 method for MultivariateAnalysis wrap_tolerance(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95)  # S4 method for MultivariateAnalysis wrap_hull(x, margin = 1, axes = c(1, 2), group = NULL)"},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrap Observations — wrap","text":"x object wrap observations (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. group vector specifying group observation belongs . level numeric vector specifying confidence/tolerance level.","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrap Observations — wrap","text":"wrap_*() returns data.frame envelope x y coordinates. extra column named group added specifying group observation belongs .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Wrap Observations — wrap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/dimensio/dev/reference/wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrap Observations — wrap","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Convex hull coordinates hulls <- wrap_hull(X, margin = 1, group = iris$Species)  ## Confidence ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species,                         level = c(0.68, 0.95))  ## Tolerance ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species, level = 0.95)  ## Plot with convex hulls col <- c(\"#004488\", \"#DDAA33\", \"#BB5566\") viz_rows(X, highlight = iris$Species, col = col) viz_hull(X, group = iris$Species, border = col)"}]