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index 728ac09e4..389c29f64 100644
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diff --git a/nexus/articles/nexus_files/figure-html/ceramics-pca-2.png b/nexus/articles/nexus_files/figure-html/ceramics-pca-2.png
index 9014e266a..0549a0750 100644
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diff --git a/nexus/favicon-16x16.png b/nexus/favicon-16x16.png
index e10313031..947e0e0f3 100644
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diff --git a/nexus/favicon-32x32.png b/nexus/favicon-32x32.png
index 20289fe25..ac6b86591 100644
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diff --git a/nexus/pkgdown.yml b/nexus/pkgdown.yml
index a0feec5d8..ce7141e61 100644
--- a/nexus/pkgdown.yml
+++ b/nexus/pkgdown.yml
@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   bibliography: bibliography.html
   nexus: nexus.html
-last_built: 2023-09-12T18:32Z
+last_built: 2023-09-12T19:01Z
 urls:
   reference: https://packages.tesselle.org/nexus/reference
   article: https://packages.tesselle.org/nexus/articles
diff --git a/nexus/reference/Rplot001.png b/nexus/reference/Rplot001.png
index 17a358060..c1ed484f7 100644
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diff --git a/nexus/reference/Rplot002.png b/nexus/reference/Rplot002.png
index b917c3e8e..0f4b13d87 100644
Binary files a/nexus/reference/Rplot002.png and b/nexus/reference/Rplot002.png differ
diff --git a/nexus/reference/Rplot004.png b/nexus/reference/Rplot004.png
index a48d1d5f1..ee9a55681 100644
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diff --git a/nexus/reference/as_amounts.html b/nexus/reference/as_amounts.html
index 89e65dc17..e0ef480b9 100644
--- a/nexus/reference/as_amounts.html
+++ b/nexus/reference/as_amounts.html
@@ -100,20 +100,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_composition.html">as_composition</a></span><span class="op">(</span><span class="va">A1</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Row sums are internally stored before coercing to relative frequencies</span></span></span>
-<span class="r-in"><span><span class="co">## (use get_totals() to retrieve these values)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="totals.html">get_totals</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 200 215 271 289 272 265 227 276 217 162 297 204 340 170 221 224 176 306 258</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 242</span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## This allows to restore the source data</span></span></span>
 <span class="r-in"><span><span class="va">A2</span> <span class="op">&lt;-</span> <span class="fu">as_amounts</span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Coerce to an S3 data.frame</span></span></span>
+<span class="r-in"><span><span class="co">## Coerce to a data.frame</span></span></span>
 <span class="r-in"><span><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</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="va">X</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            V1        V2         V3         V4         V5 samples groups</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.33913043 0.0826087 0.13043478 0.05217391 0.39565217      S1   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.01801802 0.4954955 0.29729730 0.01801802 0.17117117      S2   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.07500000 0.0562500 0.20000000 0.62500000 0.04375000      S3   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.18055556 0.3935185 0.24537037 0.03703704 0.14351852      S4   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.17316017 0.1428571 0.03030303 0.25541126 0.39826840      S5   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.24712644 0.1321839 0.25000000 0.28160920 0.08908046      S6   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           V1        V2        V3         V4         V5 samples groups</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.0850000 0.3900000 0.1950000 0.24500000 0.08500000      S1   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.2279070 0.1627907 0.2697674 0.01395349 0.32558140      S2   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.2730627 0.3653137 0.1033210 0.18450185 0.07380074      S3   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.2387543 0.2179931 0.2698962 0.18685121 0.08650519      S4   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.1654412 0.2683824 0.1948529 0.15073529 0.22058824      S5   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.1622642 0.2188679 0.1433962 0.16226415 0.31320755      S6   &lt;NA&gt;</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/nexus/reference/as_composition.html b/nexus/reference/as_composition.html
index c7bf4d2ab..97481f526 100644
--- a/nexus/reference/as_composition.html
+++ b/nexus/reference/as_composition.html
@@ -140,20 +140,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu">as_composition</span><span class="op">(</span><span class="va">A1</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Row sums are internally stored before coercing to relative frequencies</span></span></span>
-<span class="r-in"><span><span class="co">## (use get_totals() to retrieve these values)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="totals.html">get_totals</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 259 143 249 178 276 201 390 235 356 336  96 239 337 257 216 234 224 222 310</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 292</span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## This allows to restore the source data</span></span></span>
 <span class="r-in"><span><span class="va">A2</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_amounts.html">as_amounts</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Coerce to an S3 data.frame</span></span></span>
+<span class="r-in"><span><span class="co">## Coerce to a data.frame</span></span></span>
 <span class="r-in"><span><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</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="va">X</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            V1         V2         V3         V4         V5 samples groups</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.09424084 0.13089005 0.44502618 0.24083770 0.08900524      S1   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.34649123 0.32017544 0.17543860 0.03947368 0.11842105      S2   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.06589147 0.16666667 0.30620155 0.13565891 0.32558140      S3   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.24813896 0.23076923 0.06947891 0.21588089 0.23573201      S4   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.26557377 0.03606557 0.11475410 0.25573770 0.32786885      S5   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.03431373 0.48529412 0.18627451 0.01960784 0.27450980      S6   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>            V1         V2         V3        V4         V5 samples groups</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.06177606 0.06177606 0.35135135 0.3281853 0.19691120      S1   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.04895105 0.04195804 0.39160839 0.4615385 0.05594406      S2   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.40160643 0.14056225 0.02409639 0.3052209 0.12851406      S3   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.07303371 0.16292135 0.27528090 0.1123596 0.37640449      S4   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.12318841 0.24637681 0.21376812 0.1630435 0.25362319      S5   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.06467662 0.29353234 0.30845771 0.1442786 0.18905473      S6   &lt;NA&gt;</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/nexus/reference/as_graph-1.png b/nexus/reference/as_graph-1.png
index 56222921f..72ab1a3de 100644
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diff --git a/nexus/reference/as_graph-2.png b/nexus/reference/as_graph-2.png
index 3b9072631..9a14bf882 100644
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diff --git a/nexus/reference/as_graph-3.png b/nexus/reference/as_graph-3.png
index 3decf4dff..ca034e025 100644
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diff --git a/nexus/reference/as_graph-4.png b/nexus/reference/as_graph-4.png
index 553c157e2..ac1734693 100644
Binary files a/nexus/reference/as_graph-4.png and b/nexus/reference/as_graph-4.png differ
diff --git a/nexus/reference/closure.html b/nexus/reference/closure.html
index 229b61830..ac9b1ebb4 100644
--- a/nexus/reference/closure.html
+++ b/nexus/reference/closure.html
@@ -106,27 +106,32 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co">## Create a count matrix</span></span></span>
-<span class="r-in"><span><span class="va">A1</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/numeric.html" class="external-link">as.numeric</a></span><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">100</span>, <span class="fl">100</span>, <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">20</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_composition.html">as_composition</a></span><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="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_composition.html">as_composition</a></span><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="fl">1</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Coerce to compositions</span></span></span>
-<span class="r-in"><span><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_composition.html">as_composition</a></span><span class="op">(</span><span class="va">A1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="co">## Perturbation</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="perturbation.html">perturbation</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;CompositionMatrix: 1 x 3&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>       V1  V2    V3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.125 0.5 0.375</span>
+<span class="r-in"><span><span class="va">x</span> <span class="op">+</span> <span class="va">y</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;CompositionMatrix: 1 x 3&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>       V1  V2    V3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.125 0.5 0.375</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Row sums are internally stored before coercing to relative frequencies</span></span></span>
-<span class="r-in"><span><span class="co">## (use get_totals() to retrieve these values)</span></span></span>
-<span class="r-in"><span><span class="co">## This allows to restore the source data</span></span></span>
-<span class="r-in"><span><span class="va">A2</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_amounts.html">as_amounts</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="co">## Powering</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="powering.html">powering</a></span><span class="op">(</span><span class="va">y</span>, <span class="fl">2</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;CompositionMatrix: 1 x 3&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           V1        V2        V3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.1666667 0.6666667 0.1666667</span>
+<span class="r-in"><span><span class="va">y</span> <span class="op">*</span> <span class="fl">2</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;CompositionMatrix: 1 x 3&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           V1        V2        V3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.1666667 0.6666667 0.1666667</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Coerce to an S3 data.frame</span></span></span>
-<span class="r-in"><span><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</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="va">X</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            V1          V2         V3        V4         V5 samples groups</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.21008403 0.239495798 0.02941176 0.1680672 0.35294118      S1   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.11462451 0.272727273 0.24901186 0.1739130 0.18972332      S2   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.02830189 0.070754717 0.08962264 0.4103774 0.40094340      S3   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.27240143 0.003584229 0.34767025 0.2724014 0.10394265      S4   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.08542714 0.185929648 0.36180905 0.2763819 0.09045226      S5   &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.14285714 0.043650794 0.32539683 0.3531746 0.13492063      S6   &lt;NA&gt;</span>
+<span class="r-in"><span><span class="co">## Scalar product</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="scalar.html">scalar</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.06646867</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/nexus/reference/dist-1.png b/nexus/reference/dist-1.png
new file mode 100644
index 000000000..25171d35c
Binary files /dev/null and b/nexus/reference/dist-1.png differ
diff --git a/nexus/reference/dist.html b/nexus/reference/dist.html
index 4f3be4b02..69596ecb8 100644
--- a/nexus/reference/dist.html
+++ b/nexus/reference/dist.html
@@ -126,120 +126,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="st">"hongite"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">coda</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_composition.html">as_composition</a></span><span class="op">(</span><span class="va">hongite</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Distance</span></span></span>
-<span class="r-in"><span><span class="fu">dist</span><span class="op">(</span><span class="va">coda</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            H1        H2        H3        H4        H5        H6        H7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H2  0.8767510                                                            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H3  2.5214253 1.6650912                                                  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H4  0.7906400 0.2952543 1.8639740                                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H5  0.4739924 1.1608245 2.8213666 0.9854538                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H6  0.9678208 1.0017438 2.4503854 0.7106924 0.8126273                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H7  0.8391283 0.9264506 2.4466687 0.6726300 0.6518013 0.3068355          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H8  3.0681017 2.2077617 0.5985190 2.3755249 3.3531743 2.9031508 2.9350491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H9  2.1567755 1.3025730 0.3646671 1.5092037 2.4608605 2.1188602 2.1067706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H10 1.5855253 2.3852360 4.0452164 2.1890800 1.2401119 1.7847939 1.7285362</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H11 1.1710927 0.3101166 1.3704961 0.5142905 1.4599034 1.1921282 1.1626052</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H12 0.3727262 1.2405255 2.8797113 1.1509884 0.4539174 1.1923398 1.0473042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H13 2.6796901 1.8462061 0.2974720 2.0700018 3.0032107 2.6883648 2.6669260</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H14 2.1787003 1.3234987 0.8580849 1.4065875 2.3581311 1.8190796 1.8478752</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H15 1.4784947 0.6708213 1.0579099 0.9297665 1.8185451 1.6194568 1.5714340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H16 2.9044778 2.0288430 0.6027605 2.1709606 3.1392211 2.6453694 2.6681226</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H17 1.4546884 2.1462022 3.7770078 1.9169098 1.0304796 1.4107166 1.3729948</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H18 0.5245452 1.0606339 2.7007677 0.8376898 0.2629770 0.5769217 0.4884938</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H19 1.7602135 0.9534581 0.8105519 1.2096096 2.0997516 1.8869609 1.8364379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H20 2.1115841 1.2415737 0.5413915 1.3933788 2.3711124 1.9319745 1.9510350</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H21 1.6327549 0.7593779 0.9906382 0.9018811 1.8714132 1.4647356 1.4581381</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H22 0.8008035 0.4134000 1.9478272 0.1333961 0.9287606 0.5909027 0.5497058</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H23 0.7451072 1.2918095 2.9137094 1.0529779 0.3583952 0.6293206 0.5735426</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H24 1.6082346 0.7503990 0.9163484 0.9700216 1.9090190 1.6170556 1.5859866</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H25 0.9383107 0.1051583 1.6279995 0.3274461 1.1953636 1.0126284 0.9224570</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            H8        H9       H10       H11       H12       H13       H14</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H2                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H3                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H4                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H5                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H6                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H7                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H8                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H9  0.9397076                                                            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H10 4.5619484 3.6846865                                                  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H11 1.9033794 1.0096405 2.6762596                                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H12 3.4332992 2.5152272 1.2980229 1.5387862                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H13 0.6382499 0.5712106 4.2301042 1.5639306 3.0279063                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H14 1.1707267 0.7298713 3.5473128 1.0636854 2.5411784 1.1433041          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H15 1.6287975 0.6973619 3.0370608 0.4288520 1.8301814 1.2017611 1.0344674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H16 0.4545771 0.8638763 4.3488475 1.7374933 3.2690987 0.7689998 0.8360769</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H17 4.2728994 3.4243251 0.4863777 2.4251205 1.2552674 3.9824638 3.2094091</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H18 3.2105210 2.3441270 1.3561461 1.3391357 0.6366131 2.8999267 2.1988548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H19 1.3980195 0.4714590 3.3222469 0.7032641 2.1054467 0.9228103 0.9859377</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H20 0.9877633 0.3329260 3.5815737 0.9408789 2.4792771 0.8218637 0.4308744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H21 1.4914563 0.6632621 3.0879216 0.4819969 1.9980537 1.2277365 0.5920798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H22 2.4502129 1.5980522 2.1227178 0.6233978 1.1448814 2.1615401 1.4406247</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H23 3.4120401 2.5610218 1.1797726 1.5632549 0.7625165 3.1207729 2.3708636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H24 1.4738079 0.5530808 3.1343759 0.4647774 1.9682225 1.1021347 0.8059578</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H25 2.1729555 1.2681367 2.4277793 0.3059376 1.2955966 1.8116276 1.2646069</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           H15       H16       H17       H18       H19       H20       H21</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H2                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H3                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H4                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H5                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H6                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H7                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H8                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H9                                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H10                                                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H11                                                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H12                                                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H13                                                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H14                                                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H15                                                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H16 1.5257009                                                            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H17 2.8148034 4.0297987                                                  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H18 1.7276112 2.9936000 1.0890523                                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H19 0.2925366 1.3251713 3.0989710 2.0135135                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H20 0.7661338 0.8061360 3.2888972 2.2273992 0.6511045                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H21 0.4989043 1.2756316 2.7966378 1.7339926 0.5870924 0.5064689          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H22 1.0426202 2.2267808 1.8298329 0.7673337 1.3160904 1.4643450 0.9696446</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H23 1.9607003 3.1805225 0.8658073 0.2491895 2.2446978 2.4281713 1.9360069</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H24 0.2290380 1.3313017 2.8816469 1.7984553 0.3052918 0.5584031 0.3003119</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H25 0.6589617 1.9776437 2.1771982 1.0988769 0.9297336 1.2023185 0.7102152</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           H22       H23       H24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H2                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H3                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H4                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H5                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H6                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H7                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H8                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H9                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H10                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H11                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H12                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H13                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H14                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H15                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H16                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H17                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H18                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H19                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H20                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H21                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H22                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H23 0.9671322                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H24 1.0663845 2.0204099          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> H25 0.4247642 1.3238750 0.7183694</span>
+<span class="r-in"><span><span class="co">## Aitchison distance</span></span></span>
+<span class="r-in"><span><span class="co">## (euclidean distance between CLR-transformed compositions)</span></span></span>
+<span class="r-in"><span><span class="va">d</span> <span class="op">&lt;-</span> <span class="fu">dist</span><span class="op">(</span><span class="va">coda</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Mahalanobis distance</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="mahalanobis.html">mahalanobis</a></span><span class="op">(</span><span class="va">coda</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         H1         H2         H3         H4         H5         H6         H7 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  4.3305730  0.6772554  1.9637780  2.2774149  7.8532393  6.1201376 12.4124788 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         H8         H9        H10        H11        H12        H13        H14 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18.1881032  1.3256717 35.6837646  2.0679911  4.3697727  5.3058629  5.8192527 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        H15        H16        H17        H18        H19        H20        H21 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2834988  6.8917976  5.9335681  2.2112435  3.0817805  4.2738502  1.9289961 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        H22        H23        H24        H25 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3.9944644 21.1576631  3.8578108  3.4320406 </span>
+<span class="r-in"><span><span class="co">## Cluster dendrogram</span></span></span>
+<span class="r-in"><span><span class="va">h</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/hclust.html" class="external-link">hclust</a></span><span class="op">(</span><span class="va">d</span>, method <span class="op">=</span> <span class="st">"ward.D2"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">h</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="dist-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/nexus/reference/mahalanobis.html b/nexus/reference/mahalanobis.html
index 24eaaa577..8794666db 100644
--- a/nexus/reference/mahalanobis.html
+++ b/nexus/reference/mahalanobis.html
@@ -118,6 +118,25 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>N. Frerebeau</p>
     </div>
 
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co">## Coerce to compositional data</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="st">"hongite"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">coda</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_composition.html">as_composition</a></span><span class="op">(</span><span class="va">hongite</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">## Mahalanobis distance</span></span></span>
+<span class="r-in"><span><span class="fu">mahalanobis</span><span class="op">(</span><span class="va">coda</span><span class="op">)</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>         H1         H2         H3         H4         H5         H6         H7 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5.4655057  0.4098836  2.2782942  1.3153083 15.7930890  5.9302032 32.6554433 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>         H8         H9        H10        H11        H12        H13        H14 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24.7487338  1.3415634 25.3311592  1.8975808  6.2480419  4.5679191 20.6506579 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        H15        H16        H17        H18        H19        H20        H21 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7973233 20.7950173  8.1328081  1.3600287  2.5432737  4.5402709  2.3044368 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        H22        H23        H24        H25 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4282370 10.0877414  3.6234908  6.7280891 </span>
+</code></pre></div>
+    </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside></div>
 
diff --git a/nexus/reference/outliers-1.png b/nexus/reference/outliers-1.png
index 8b6614dc1..73f7cf4b0 100644
Binary files a/nexus/reference/outliers-1.png and b/nexus/reference/outliers-1.png differ
diff --git a/nexus/reference/outliers-2.png b/nexus/reference/outliers-2.png
index 957bdf99f..9e693d147 100644
Binary files a/nexus/reference/outliers-2.png and b/nexus/reference/outliers-2.png differ
diff --git a/nexus/reference/pca_coda-2.png b/nexus/reference/pca_coda-2.png
index 9a1539204..fd78b7419 100644
Binary files a/nexus/reference/pca_coda-2.png and b/nexus/reference/pca_coda-2.png differ
diff --git a/nexus/reference/plot_outliers-1.png b/nexus/reference/plot_outliers-1.png
index a4fddbbdb..bfb9bb1ec 100644
Binary files a/nexus/reference/plot_outliers-1.png and b/nexus/reference/plot_outliers-1.png differ
diff --git a/nexus/reference/plot_outliers-2.png b/nexus/reference/plot_outliers-2.png
index 8c7df58ad..07bc00c53 100644
Binary files a/nexus/reference/plot_outliers-2.png and b/nexus/reference/plot_outliers-2.png differ
diff --git a/nexus/reference/totals.html b/nexus/reference/totals.html
index fa6da6199..f09b5e053 100644
--- a/nexus/reference/totals.html
+++ b/nexus/reference/totals.html
@@ -98,6 +98,34 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>N. Frerebeau</p>
     </div>
 
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co">## Create a count matrix</span></span></span>
+<span class="r-in"><span><span class="va">A1</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/numeric.html" class="external-link">as.numeric</a></span><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">100</span>, <span class="fl">100</span>, <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">20</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">## Coerce to compositions</span></span></span>
+<span class="r-in"><span><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_composition.html">as_composition</a></span><span class="op">(</span><span class="va">A1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">## Row sums are internally stored before coercing to relative frequencies</span></span></span>
+<span class="r-in"><span><span class="fu">get_totals</span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 120 182 215 197 209 257 262 182 283 378 290 232 165 221 273 406 307 272 337</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 233</span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">## This allows to restore the source data</span></span></span>
+<span class="r-in"><span><span class="va">A2</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_amounts.html">as_amounts</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">## Coerce to a data.frame</span></span></span>
+<span class="r-in"><span><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</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="va">X</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>            V1         V2         V3         V4         V5 samples groups</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.53333333 0.30833333 0.03333333 0.06666667 0.05833333      S1   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.15934066 0.49450549 0.15384615 0.06593407 0.12637363      S2   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.02325581 0.05581395 0.37209302 0.31162791 0.23720930      S3   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.03553299 0.32487310 0.29441624 0.22842640 0.11675127      S4   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.29665072 0.20574163 0.22966507 0.22009569 0.04784689      S5   &lt;NA&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.08560311 0.31906615 0.03501946 0.38521401 0.17509728      S6   &lt;NA&gt;</span>
+</code></pre></div>
+    </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside></div>
 
diff --git a/nexus/reference/variation-1.png b/nexus/reference/variation-1.png
new file mode 100644
index 000000000..11cc4205c
Binary files /dev/null and b/nexus/reference/variation-1.png differ
diff --git a/nexus/reference/variation.html b/nexus/reference/variation.html
index 9cc93baef..faf2bd93d 100644
--- a/nexus/reference/variation.html
+++ b/nexus/reference/variation.html
@@ -110,13 +110,19 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Variation matrix</span></span></span>
 <span class="r-in"><span><span class="co">## (Aitchison 1986, definition 4.4)</span></span></span>
-<span class="r-in"><span><span class="fu">variation</span><span class="op">(</span><span class="va">coda</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">(</span><span class="va">varia</span> <span class="op">&lt;-</span> <span class="fu">variation</span><span class="op">(</span><span class="va">coda</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>            A         B         C          D         E</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> A 0.00000000 0.2592742 1.5328586 0.08281464 0.1385604</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> B 0.25927416 0.0000000 3.0006804 0.54727146 0.6490135</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> C 1.53285862 3.0006804 0.0000000 1.11145146 0.9476375</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> D 0.08281464 0.5472715 1.1114515 0.00000000 0.1870579</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> E 0.13856035 0.6490135 0.9476375 0.18705791 0.0000000</span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">## Cluster dendrogram</span></span></span>
+<span class="r-in"><span><span class="va">d</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/dist.html" class="external-link">as.dist</a></span><span class="op">(</span><span class="va">varia</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">h</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/hclust.html" class="external-link">hclust</a></span><span class="op">(</span><span class="va">d</span>, method <span class="op">=</span> <span class="st">"ward.D2"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">h</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="variation-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/nexus/search.json b/nexus/search.json
index fd6cda329..b865b5d47 100644
--- a/nexus/search.json
+++ b/nexus/search.json
@@ -1 +1 @@
-[{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"get-started","dir":"Articles","previous_headings":"","what":"Get started","title":"Introduction to nexus","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e. quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986; Greenacre 2021). assumes keep data tidy: variable must saved column observation (sample) must saved row. class simple use inherits base matrix: CompositionMatrix represents closed composition matrix: row matrix sum 1 (relative changes relevant compositional data analysis). original row sums kept internally, source data can restored:","code":"library(nexus) ## Mineral compositions of rock specimens data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Coerce to count data counts <- as_amounts(coda)  all.equal(hongite, as.data.frame(counts)) #> [1] TRUE ## Compositional bar plot plot(coda, order = \"A\")"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-reference-groups","dir":"Articles","previous_headings":"Get started","what":"Working with (reference) groups","title":"Introduction to nexus","text":"Provenance studies typically rely two approaches, can used together: Identification groups among artifacts studied, based mineralogical geochemical criteria (clustering). Comparison -called reference groups, .e. known geological sources productive contexts (classification). nexus allows specify whether observation belongs specific group (). coercing data.frame CompositionMatrix object, attempt made automatically detect groups mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used groups argument as_composition(). get_groups(x) set_groups(x) <- value allow retrieve set groups existing CompositionMatrix (NA can used specify sample belong group): groups defined, can used methods (e.g. plotting).","code":"## Create a data.frame X <- data.frame(   groups = c(\"A\", \"A\", \"B\", \"A\", \"B\", \"C\", \"C\", \"C\", \"B\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) has_groups(Y) #> [1] TRUE ## Set groups (NA means no group) set_groups(Y) <- c(\"X\", \"X\", \"Y\", \"X\", \"Y\", NA, NA, NA, \"Y\")  ## Retrieve groups get_groups(Y) #> [1] \"X\" \"X\" \"Y\" \"X\" \"Y\" NA  NA  NA  \"Y\""},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-repeated-measurements","dir":"Articles","previous_headings":"Get started","what":"Working with repeated measurements","title":"Introduction to nexus","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. coercing data.frame CompositionMatrix object, attempt made automatically detect samples mapping column names. matching column found, row names used default. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used samples argument as_composition(). get_samples(x) set_samples(x) <- value allow retrieve set sample names existing CompositionMatrix (missing values allowed): Note presence repeated measurements may affect calculations (read documentation carefully).","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) has_replicates(Y) #> [1] TRUE ## Set sample names set_samples(Y) <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\")  ## Retrieve groups get_samples(Y) #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\""},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"log-ratio-transformations","dir":"Articles","previous_headings":"Get started","what":"Log-ratio transformations","title":"Introduction to nexus","text":"amount every part limited whole. absolute amount whole noninformative since typically due artifacts measurement procedure. Thus relative changes relevant. relative amount one part increases, amounts parts must decrease, introducing spurious anticorrelation.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"ceramic-compositions","dir":"Articles","previous_headings":"Use cases","what":"Ceramic compositions","title":"Introduction to nexus","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\")  ## Remove rows with missing values kommos <- remove_NA(kommos, margin = 1)  ## Coerce to a compositional matrix coda <- as_composition(kommos) #> 2 qualitative variables were removed: type, date.  ## Set groups set_groups(coda) <- kommos$type ## Compositional bar plot plot(coda, order = \"Ca\") ## CLR clr <- transform_clr(coda) plot(clr, border = c(\"#EE7733\", \"#0077BB\", \"#33BBEE\", \"#EE3377\")) ## PCA clr_pca <- pca(clr, scale = FALSE)  viz_individuals(clr_pca, highlight = get_groups(coda), pch = 16,                  col = c(\"#EE7733\", \"#0077BB\", \"#33BBEE\", \"#EE3377\")) viz_variables(clr_pca)"},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/nexus/index.html","id":"nexus-","dir":"","previous_headings":"","what":"Sourcing Archaeological Materials by Chemical Composition","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Exploration analysis compositional data framework Aitchison (1986). package provides tools chemical fingerprinting source tracking ancient materials. Initial development progress.","code":"To cite nexus in publications use:    Frerebeau N (2023). _nexus: Sourcing Archaeological Materials by   Chemical Composition_. Université Bordeaux Montaigne, Pessac, France.   R package version 0.0.0.9000, <https://packages.tesselle.org/nexus/>.  Une entrée BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{nexus: Sourcing Archaeological Materials by Chemical Composition}},     year = {2023},     organization = {Université Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.0.0.9000},     url = {https://packages.tesselle.org/nexus/},   }  This package is a part of the tesselle project <https://www.tesselle.org>."},{"path":"https://packages.tesselle.org/nexus/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Sourcing Archaeological Materials by Chemical Composition","text":"can install released version nexus CRAN : development version GitHub :","code":"install.packages(\"nexus\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/nexus\")"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Sourcing Archaeological Materials by Chemical Composition","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e. quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986). assumes keep data tidy: variable must saved column observation (sample) must saved row. new classes simple use inherit base matrix: CompositionMatrix class special slots (see vignette(\"nexus\")): samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE).","code":"## Load the package library(nexus) ## Mineral compositions of rock specimens data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Get samples get_samples(Y) #> [1] \"A\" \"A\" \"A\" \"B\" \"B\" \"B\" \"C\" \"C\" \"C\"  ## Get groups get_groups(Y) #> [1] \"X\" \"X\" \"X\" \"X\" \"X\" \"X\" \"Y\" \"Y\" \"Y\""},{"path":"https://packages.tesselle.org/nexus/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Please note nexus project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Matrix — CompositionMatrix-class","title":"Compositional Matrix — CompositionMatrix-class","text":"S4 class represent compositional data.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Compositional Matrix — CompositionMatrix-class","text":"totals numeric vector store absolute row sums (closure compositions). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names ().","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compositional Matrix — CompositionMatrix-class","text":"class inherits matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Compositional Matrix — CompositionMatrix-class","text":"code snippets , x CompositionMatrix object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Matrix — CompositionMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Matrix — CompositionMatrix-class","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## codaccess dim(coda) # Get the matrix dimensions #> [1] 25  5 row(coda) # Get the row indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,]    1    1    1    1    1 #>  [2,]    2    2    2    2    2 #>  [3,]    3    3    3    3    3 #>  [4,]    4    4    4    4    4 #>  [5,]    5    5    5    5    5 #>  [6,]    6    6    6    6    6 #>  [7,]    7    7    7    7    7 #>  [8,]    8    8    8    8    8 #>  [9,]    9    9    9    9    9 #> [10,]   10   10   10   10   10 #> [11,]   11   11   11   11   11 #> [12,]   12   12   12   12   12 #> [13,]   13   13   13   13   13 #> [14,]   14   14   14   14   14 #> [15,]   15   15   15   15   15 #> [16,]   16   16   16   16   16 #> [17,]   17   17   17   17   17 #> [18,]   18   18   18   18   18 #> [19,]   19   19   19   19   19 #> [20,]   20   20   20   20   20 #> [21,]   21   21   21   21   21 #> [22,]   22   22   22   22   22 #> [23,]   23   23   23   23   23 #> [24,]   24   24   24   24   24 #> [25,]   25   25   25   25   25 col(coda, as.factor = TRUE) # Get the column indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,] A    B    C    D    E    #>  [2,] A    B    C    D    E    #>  [3,] A    B    C    D    E    #>  [4,] A    B    C    D    E    #>  [5,] A    B    C    D    E    #>  [6,] A    B    C    D    E    #>  [7,] A    B    C    D    E    #>  [8,] A    B    C    D    E    #>  [9,] A    B    C    D    E    #> [10,] A    B    C    D    E    #> [11,] A    B    C    D    E    #> [12,] A    B    C    D    E    #> [13,] A    B    C    D    E    #> [14,] A    B    C    D    E    #> [15,] A    B    C    D    E    #> [16,] A    B    C    D    E    #> [17,] A    B    C    D    E    #> [18,] A    B    C    D    E    #> [19,] A    B    C    D    E    #> [20,] A    B    C    D    E    #> [21,] A    B    C    D    E    #> [22,] A    B    C    D    E    #> [23,] A    B    C    D    E    #> [24,] A    B    C    D    E    #> [25,] A    B    C    D    E    #> Levels: A B C D E nrow(coda) # Get the number of rows #> [1] 25 ncol(coda) # Get the number of columns #> [1] 5 dimnames(coda) # Get the dimension names #> [[1]] #>  [1] \"H1\"  \"H2\"  \"H3\"  \"H4\"  \"H5\"  \"H6\"  \"H7\"  \"H8\"  \"H9\"  \"H10\" \"H11\" \"H12\" #> [13] \"H13\" \"H14\" \"H15\" \"H16\" \"H17\" \"H18\" \"H19\" \"H20\" \"H21\" \"H22\" \"H23\" \"H24\" #> [25] \"H25\" #>  #> [[2]] #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" #>  rownames(coda) <- LETTERS[1:25] # Set the row names rownames(coda) # Get the rownames #>  [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" colnames(coda) <- letters[21:25] # Set the column names colnames(coda) # Get the column names #> [1] \"u\" \"v\" \"w\" \"x\" \"y\""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Ratio Matrix — LogRatio-class","title":"Log-Ratio Matrix — LogRatio-class","text":"S4 classes represent log-ratio data transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Log-Ratio Matrix — LogRatio-class","text":"totals numeric vector store absolute row sums (closure compositions). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). parts character vector store part names. ratio character vector store ratio names. order integer vector store original ordering columns. base numeric matrix store basis transformation. weights numeric vector store weights assigned respective log-ratios.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log-Ratio Matrix — LogRatio-class","text":"classes inherit matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Log-Ratio Matrix — LogRatio-class","text":"code snippets , x LogRatio object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Log-Ratio Matrix — LogRatio-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical Matrix — LogicalMatrix-class","title":"Logical Matrix — LogicalMatrix-class","text":"S4 classes represent \\(m \\times p\\) logical matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Logical Matrix — LogicalMatrix-class","text":".Data \\(m \\times p\\) logical matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logical Matrix — LogicalMatrix-class","text":"class inherits matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Logical Matrix — LogicalMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Matrix — NumericMatrix-class","title":"Numeric Matrix — NumericMatrix-class","text":"S4 classes represent \\(m \\times p\\) numeric matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Numeric Matrix — NumericMatrix-class","text":".Data \\(m \\times p\\) numeric matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Numeric Matrix — NumericMatrix-class","text":"class inherits matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Numeric Matrix — NumericMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Outliers — OutlierIndex-class","title":"Outliers — OutlierIndex-class","text":"S4 class store result outlier detection.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Outliers — OutlierIndex-class","text":".Data logical vector. samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). distances numeric vector giving squared Mahalanobis distance. limit numeric value giving cut-value used outlier detection (quantile Chi-squared distribution). robust logical scalar: robust estimators used? dof (non-negative) numeric value giving degrees freedom.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outliers — OutlierIndex-class","text":"classes inherit logical.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Outliers — OutlierIndex-class","text":"code snippets , x OutlierIndex object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outliers — OutlierIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Summary Statistics of Data Subsets — aggregate","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"Splits data subsets, computes summary statistics , returns result.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"","code":"# S4 method for CompositionMatrix aggregate(x, by, FUN, ...)"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"x CompositionMatrix object. character string specifying grouping element. must one \"samples\" \"groups\". unambiguous substring can given. FUN function compute summary statistics. ... arguments passed FUN.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Compositional mean by sample aggregate(Y, by = \"samples\", FUN = mean) #>          Ca        Fe         Na #> A 0.4468157 0.4539051 0.09927924 #> B 0.4746506 0.4742013 0.05114805 #> C 0.3067545 0.6238351 0.06941036  ## Compositional mean by group aggregate(Y, by = \"groups\", FUN = mean) #>          Ca        Fe         Na #> X 0.4625002 0.4659342 0.07156562 #> Y 0.3067545 0.6238351 0.06941036"},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":null,"dir":"Reference","previous_headings":"","what":"Operations in the Simplex — arithmetic","title":"Operations in the Simplex — arithmetic","text":"Operators performing operations simplex.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Operations in the Simplex — arithmetic","text":"","code":"# S4 method for CompositionMatrix,CompositionMatrix +(e1, e2)  # S4 method for CompositionMatrix,CompositionMatrix -(e1, e2)  # S4 method for CompositionMatrix,numeric *(e1, e2)  # S4 method for numeric,CompositionMatrix *(e1, e2)"},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Operations in the Simplex — arithmetic","text":"e1 CompositionMatrix object. e2 CompositionMatrix object numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Operations in the Simplex — arithmetic","text":"CompositionMatrix object numeric vector (e1).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Operations in the Simplex — arithmetic","text":"+ Perturbation operation. - Perturbation operation. * Powering operation.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Operations in the Simplex — arithmetic","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Operations in the Simplex — arithmetic","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Amounts — as_amounts","title":"Coerce to Amounts — as_amounts","text":"Coerce Amounts","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Amounts — as_amounts","text":"","code":"as_amounts(from, ...)  # S4 method for CompositionMatrix as_amounts(from)"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Amounts — as_amounts","text":"CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Amounts — as_amounts","text":"numeric  matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Amounts — as_amounts","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Amounts — as_amounts","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>            V1        V2         V3         V4         V5 samples groups #> S1 0.33913043 0.0826087 0.13043478 0.05217391 0.39565217      S1   <NA> #> S2 0.01801802 0.4954955 0.29729730 0.01801802 0.17117117      S2   <NA> #> S3 0.07500000 0.0562500 0.20000000 0.62500000 0.04375000      S3   <NA> #> S4 0.18055556 0.3935185 0.24537037 0.03703704 0.14351852      S4   <NA> #> S5 0.17316017 0.1428571 0.03030303 0.25541126 0.39826840      S5   <NA> #> S6 0.24712644 0.1321839 0.25000000 0.28160920 0.08908046      S6   <NA>"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Closed Compositional Matrix — as_composition","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"Coerces object CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"","code":"as_composition(from, ...)  # S4 method for numeric as_composition(from)  # S4 method for matrix as_composition(from)  # S4 method for data.frame as_composition(   from,   samples = NULL,   groups = NULL,   verbose = getOption(\"nexus.verbose\") )"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"matrix data.frame coerced. ... Currently used. samples integer giving index column used sample identification: allows identify replicated measurements. NULL (default), row names used sample IDs. groups integer giving index column used group samples. NULL (default), grouping stored. verbose logical scalar: R report extra information progress?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"CompositionMatrix class special slots: samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names (case insensitive, plural insensitive). behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying columns used. See vignette(\"nexus\").","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"non-numeric variable removed.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>            V1         V2         V3         V4         V5 samples groups #> S1 0.09424084 0.13089005 0.44502618 0.24083770 0.08900524      S1   <NA> #> S2 0.34649123 0.32017544 0.17543860 0.03947368 0.11842105      S2   <NA> #> S3 0.06589147 0.16666667 0.30620155 0.13565891 0.32558140      S3   <NA> #> S4 0.24813896 0.23076923 0.06947891 0.21588089 0.23573201      S4   <NA> #> S5 0.26557377 0.03606557 0.11475410 0.25573770 0.32786885      S5   <NA> #> S6 0.03431373 0.48529412 0.18627451 0.01960784 0.27450980      S6   <NA>"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph of Log-ratios — as_graph","title":"Graph of Log-ratios — as_graph","text":"Produces graph log-ratios.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph of Log-ratios — as_graph","text":"","code":"as_graph(object, ...)  # S4 method for LR as_graph(object)  # S4 method for ALR as_graph(object)  # S4 method for ILR as_graph(object)"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph of Log-ratios — as_graph","text":"object LogRatio object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph of Log-ratios — as_graph","text":"igraph graph object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graph of Log-ratios — as_graph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph of Log-ratios — as_graph","text":"","code":"library(igraph) #>  #> Attaching package: ‘igraph’ #> The following objects are masked from ‘package:stats’: #>  #>     decompose, spectrum #> The following object is masked from ‘package:base’: #>  #>     union  ## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda) lr_graph <- as_graph(lr) plot(lr_graph)   ## Additive log-ratio alr <- transform_alr(coda) alr_graph <- as_graph(alr) plot(alr_graph)   ## Isometric log-ratio ilr <- transform_ilr(coda) ilr_graph <- as_graph(ilr) plot(ilr_graph)   plr <- transform_plr(coda) plr_graph <- as_graph(plr) plot(plr_graph)"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":null,"dir":"Reference","previous_headings":"","what":"Closure Operation — closure","title":"Closure Operation — closure","text":"Closes compositions sum 1.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Closure Operation — closure","text":"","code":"closure(x, ...)  # S4 method for numeric closure(x, total = 1, na.rm = FALSE)  # S4 method for matrix closure(x, total = 1, na.rm = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Closure Operation — closure","text":"x numeric vector matrix. ... Currently used. total numeric vector specifying total amount compositions closed (defaults 1). na.rm logical scalar: missing values removed?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Closure Operation — closure","text":"numeric vector matrix (x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Closure Operation — closure","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Closure Operation — closure","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>            V1          V2         V3        V4         V5 samples groups #> S1 0.21008403 0.239495798 0.02941176 0.1680672 0.35294118      S1   <NA> #> S2 0.11462451 0.272727273 0.24901186 0.1739130 0.18972332      S2   <NA> #> S3 0.02830189 0.070754717 0.08962264 0.4103774 0.40094340      S3   <NA> #> S4 0.27240143 0.003584229 0.34767025 0.2724014 0.10394265      S4   <NA> #> S5 0.08542714 0.185929648 0.36180905 0.2763819 0.09045226      S5   <NA> #> S6 0.14285714 0.043650794 0.32539683 0.3531746 0.13492063      S6   <NA>"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":null,"dir":"Reference","previous_headings":"","what":"Covariance Matrix — covariance","title":"Covariance Matrix — covariance","text":"Computes (centered) log-ratio covariance matrix (see ).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Covariance Matrix — covariance","text":"","code":"covariance(x, ...)  # S4 method for CompositionMatrix covariance(x, center = FALSE, method = \"pearson\")  # S4 method for ALR covariance(x, method = \"pearson\")  # S4 method for CLR covariance(x, method = \"pearson\")"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Covariance Matrix — covariance","text":"x CompositionMatrix object. ... Currently used. center logical scalar: centered log-ratio covariance matrix computed? method character string indicating covariance computed (see stats::cov()).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Covariance Matrix — covariance","text":"matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Covariance Matrix — covariance","text":"covariance(ALR): Computes log-ratio covariance matrix (Aitchison 1986, definition 4.5). covariance(CLR): Computes centered log-ratio covariance matrix (Aitchison 1986, definition 4.6).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Covariance Matrix — covariance","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Covariance Matrix — covariance","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Covariance Matrix — covariance","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Log-ratio covariance matrix ## (Aitchison 1986, definition 4.5) covariance(coda, center = FALSE) #>            A_E        B_E        C_E       D_E #> A_E  0.1385604  0.2641498 -0.2233304 0.1214018 #> B_E  0.2641498  0.6490135 -0.7020147 0.1444000 #> C_E -0.2233304 -0.7020147  0.9476375 0.0116220 #> D_E  0.1214018  0.1444000  0.0116220 0.1870579  ## Centered log-ratio covariance matrix ## (Aitchison 1986, definition 4.6) covariance(coda, center = TRUE) #>             A           B           C           D           E #> A  0.06443676  0.17907284 -0.24408053  0.01453821 -0.01396727 #> B  0.17907284  0.55298309 -0.73371823  0.02658296 -0.02492066 #> C -0.24408053 -0.73371823  0.98026080 -0.04186819  0.03940616 #> D  0.01453821  0.02658296 -0.04186819  0.04745430 -0.04670728 #> E -0.01396727 -0.02492066  0.03940616 -0.04670728  0.04618906"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Distances — dist","title":"Distances — dist","text":"Computes log-ratio variance matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distances — dist","text":"","code":"# S4 method for CompositionMatrix dist(x, method = \"euclidean\", diag = FALSE, upper = FALSE, p = 2)"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distances — dist","text":"x CompositionMatrix object. method character string specifying distance measure used. See stats::dist() available distances. diag logical scalar indicating whether diagonal distance matrix printed. upper logical scalar indicating whether upper triangle distance matrix printed. p integer giving power Minkowski distance.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distances — dist","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distances — dist","text":"Distances computed CLR-transformed data.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Distances — dist","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distances — dist","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distances — dist","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Distance dist(coda) #>            H1        H2        H3        H4        H5        H6        H7 #> H2  0.8767510                                                             #> H3  2.5214253 1.6650912                                                   #> H4  0.7906400 0.2952543 1.8639740                                         #> H5  0.4739924 1.1608245 2.8213666 0.9854538                               #> H6  0.9678208 1.0017438 2.4503854 0.7106924 0.8126273                     #> H7  0.8391283 0.9264506 2.4466687 0.6726300 0.6518013 0.3068355           #> H8  3.0681017 2.2077617 0.5985190 2.3755249 3.3531743 2.9031508 2.9350491 #> H9  2.1567755 1.3025730 0.3646671 1.5092037 2.4608605 2.1188602 2.1067706 #> H10 1.5855253 2.3852360 4.0452164 2.1890800 1.2401119 1.7847939 1.7285362 #> H11 1.1710927 0.3101166 1.3704961 0.5142905 1.4599034 1.1921282 1.1626052 #> H12 0.3727262 1.2405255 2.8797113 1.1509884 0.4539174 1.1923398 1.0473042 #> H13 2.6796901 1.8462061 0.2974720 2.0700018 3.0032107 2.6883648 2.6669260 #> H14 2.1787003 1.3234987 0.8580849 1.4065875 2.3581311 1.8190796 1.8478752 #> H15 1.4784947 0.6708213 1.0579099 0.9297665 1.8185451 1.6194568 1.5714340 #> H16 2.9044778 2.0288430 0.6027605 2.1709606 3.1392211 2.6453694 2.6681226 #> H17 1.4546884 2.1462022 3.7770078 1.9169098 1.0304796 1.4107166 1.3729948 #> H18 0.5245452 1.0606339 2.7007677 0.8376898 0.2629770 0.5769217 0.4884938 #> H19 1.7602135 0.9534581 0.8105519 1.2096096 2.0997516 1.8869609 1.8364379 #> H20 2.1115841 1.2415737 0.5413915 1.3933788 2.3711124 1.9319745 1.9510350 #> H21 1.6327549 0.7593779 0.9906382 0.9018811 1.8714132 1.4647356 1.4581381 #> H22 0.8008035 0.4134000 1.9478272 0.1333961 0.9287606 0.5909027 0.5497058 #> H23 0.7451072 1.2918095 2.9137094 1.0529779 0.3583952 0.6293206 0.5735426 #> H24 1.6082346 0.7503990 0.9163484 0.9700216 1.9090190 1.6170556 1.5859866 #> H25 0.9383107 0.1051583 1.6279995 0.3274461 1.1953636 1.0126284 0.9224570 #>            H8        H9       H10       H11       H12       H13       H14 #> H2                                                                        #> H3                                                                        #> H4                                                                        #> H5                                                                        #> H6                                                                        #> H7                                                                        #> H8                                                                        #> H9  0.9397076                                                             #> H10 4.5619484 3.6846865                                                   #> H11 1.9033794 1.0096405 2.6762596                                         #> H12 3.4332992 2.5152272 1.2980229 1.5387862                               #> H13 0.6382499 0.5712106 4.2301042 1.5639306 3.0279063                     #> H14 1.1707267 0.7298713 3.5473128 1.0636854 2.5411784 1.1433041           #> H15 1.6287975 0.6973619 3.0370608 0.4288520 1.8301814 1.2017611 1.0344674 #> H16 0.4545771 0.8638763 4.3488475 1.7374933 3.2690987 0.7689998 0.8360769 #> H17 4.2728994 3.4243251 0.4863777 2.4251205 1.2552674 3.9824638 3.2094091 #> H18 3.2105210 2.3441270 1.3561461 1.3391357 0.6366131 2.8999267 2.1988548 #> H19 1.3980195 0.4714590 3.3222469 0.7032641 2.1054467 0.9228103 0.9859377 #> H20 0.9877633 0.3329260 3.5815737 0.9408789 2.4792771 0.8218637 0.4308744 #> H21 1.4914563 0.6632621 3.0879216 0.4819969 1.9980537 1.2277365 0.5920798 #> H22 2.4502129 1.5980522 2.1227178 0.6233978 1.1448814 2.1615401 1.4406247 #> H23 3.4120401 2.5610218 1.1797726 1.5632549 0.7625165 3.1207729 2.3708636 #> H24 1.4738079 0.5530808 3.1343759 0.4647774 1.9682225 1.1021347 0.8059578 #> H25 2.1729555 1.2681367 2.4277793 0.3059376 1.2955966 1.8116276 1.2646069 #>           H15       H16       H17       H18       H19       H20       H21 #> H2                                                                        #> H3                                                                        #> H4                                                                        #> H5                                                                        #> H6                                                                        #> H7                                                                        #> H8                                                                        #> H9                                                                        #> H10                                                                       #> H11                                                                       #> H12                                                                       #> H13                                                                       #> H14                                                                       #> H15                                                                       #> H16 1.5257009                                                             #> H17 2.8148034 4.0297987                                                   #> H18 1.7276112 2.9936000 1.0890523                                         #> H19 0.2925366 1.3251713 3.0989710 2.0135135                               #> H20 0.7661338 0.8061360 3.2888972 2.2273992 0.6511045                     #> H21 0.4989043 1.2756316 2.7966378 1.7339926 0.5870924 0.5064689           #> H22 1.0426202 2.2267808 1.8298329 0.7673337 1.3160904 1.4643450 0.9696446 #> H23 1.9607003 3.1805225 0.8658073 0.2491895 2.2446978 2.4281713 1.9360069 #> H24 0.2290380 1.3313017 2.8816469 1.7984553 0.3052918 0.5584031 0.3003119 #> H25 0.6589617 1.9776437 2.1771982 1.0988769 0.9297336 1.2023185 0.7102152 #>           H22       H23       H24 #> H2                                #> H3                                #> H4                                #> H5                                #> H6                                #> H7                                #> H8                                #> H9                                #> H10                               #> H11                               #> H12                               #> H13                               #> H14                               #> H15                               #> H16                               #> H17                               #> H18                               #> H19                               #> H20                               #> H21                               #> H22                               #> H23 0.9671322                     #> H24 1.0663845 2.0204099           #> H25 0.4247642 1.3238750 0.7183694  ## Mahalanobis distance mahalanobis(coda) #>  #>         H1         H2         H3         H4         H5         H6         H7  #>  4.3305730  0.6772554  1.9637780  2.2774149  7.8532393  6.1201376 12.4124788  #>         H8         H9        H10        H11        H12        H13        H14  #> 18.1881032  1.3256717 35.6837646  2.0679911  4.3697727  5.3058629  5.8192527  #>        H15        H16        H17        H18        H19        H20        H21  #>  2.2834988  6.8917976  5.9335681  2.2112435  3.0817805  4.2738502  1.9289961  #>        H22        H23        H24        H25  #>  3.9944644 21.1576631  3.8578108  3.4320406"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Groups — groups","title":"Working With Groups — groups","text":"Retrieves defines groups observations belong.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Groups — groups","text":"","code":"has_groups(x)  get_groups(x)  set_groups(x) <- value  # S4 method for CompositionMatrix has_groups(x)  # S4 method for LogRatio has_groups(x)  # S4 method for OutlierIndex has_groups(x)  # S4 method for CompositionMatrix get_groups(x)  # S4 method for LogRatio get_groups(x)  # S4 method for OutlierIndex get_groups(x)  # S4 method for CompositionMatrix set_groups(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Groups — groups","text":"x object get set groups. value possible value groups x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Groups — groups","text":"set_groups() returns object sort x new group names assigned. get_groups() returns group names x. has_groups() returns logical scalar.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Groups — groups","text":"See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Groups — groups","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":null,"dir":"Reference","previous_headings":"","what":"Hongite Mineralogy — hongite","title":"Hongite Mineralogy — hongite","text":"Mineral compositions 25 rock specimens hongite type.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hongite Mineralogy — hongite","text":"","code":"hongite"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hongite Mineralogy — hongite","text":"data.frame 5 variables (minerals): Albite (percent). B Blandite (percent). C Cornite (percent). D Daubite (percent). E Endite (percent).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hongite Mineralogy — hongite","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":null,"dir":"Reference","previous_headings":"","what":"Mahalanobis Distance — mahalanobis","title":"Mahalanobis Distance — mahalanobis","text":"Computes squared Mahalanobis distance rows x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mahalanobis Distance — mahalanobis","text":"","code":"# S4 method for CompositionMatrix mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))  # S4 method for ILR mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mahalanobis Distance — mahalanobis","text":"x CompositionMatrix ILR object. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mahalanobis Distance — mahalanobis","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mahalanobis Distance — mahalanobis","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Mean — mean","title":"Compositional Mean — mean","text":"Compositional Mean","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compositional Mean — mean","text":"","code":"# S4 method for CompositionMatrix mean(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compositional Mean — mean","text":"x CompositionMatrix object. na.rm logical scalar: missing values removed? ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compositional Mean — mean","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compositional Mean — mean","text":"Closed vector columns geometric means.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compositional Mean — mean","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Mean — mean","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Mean — mean","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827   ## Metric variance mvar(coda) #> [1] 1.691324  ## Metric standard deviation msd(coda) #> [1] 0.6502546"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"Exploration analysis compositional data framework Aitchison (1986) <isbn: 9780412280603>. package provides tools chemical fingerprinting source tracking ancient materials.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"nexus uses following options() configure behavior: nexus.autodetect: logical scalar. Try automatically assign values corresponding slot CompositionMatrix object coercing data.frame? Defaults TRUE. nexus.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-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/nexus/reference/outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection — outliers","title":"Outlier Detection — outliers","text":"Outlier Detection","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection — outliers","text":"","code":"outliers(object, ...)  # S4 method for CompositionMatrix outliers(   object,   center = NULL,   cov = NULL,   robust = TRUE,   method = c(\"mve\", \"mcd\"),   quantile = 0.975,   ... )"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection — outliers","text":"object CompositionMatrix. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE. quantile length-one numeric vector giving significance level. quantile used cut-value outlier detection: observations larger (squared) Mahalanobis distance considered potential outliers.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection — outliers","text":"OutlierIndex object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Outlier Detection — outliers","text":"outlier can defined large Mahalanobis distance observations. way, certain proportion observations can identified, e.g. top 2% values (.e. values 0.98th percentile Chi-2 distribution). one hand, Mahalanobis distance likely strongly affected presence outliers. Rousseeuw van Zomeren (1990) thus recommend using robust methods (excessively affected presence outliers). hand, choice threshold classifying observation outlier discussed. apparent reason particular threshold applicable data sets (Filzmoser, Garrett, Reimann 2005).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Outlier Detection — outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 . Rousseeuw, P. J. & van Zomeren, B. C. (1990). Unmasking Multivariate Outliers Leverage Points. Journal American Statistical Association, 85(411): 633-639. doi:10.1080/01621459.1990.10474920 . Santos, F. (2020). Modern methods old data: overview robust methods outliers detection applications osteology. Journal Archaeological Science: Reports, 32, 102423. doi:10.1016/j.jasrep.2020.102423 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outlier Detection — outliers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection — outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda) #>   ## Quantile-Quantile plot plot(out, qq = TRUE)   ## Plot plot(out)"},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis — pca_coda","title":"Principal Components Analysis — pca_coda","text":"Computes principal components analysis based singular value decomposition.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis — pca_coda","text":"","code":"# S4 method for CompositionMatrix pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for LogRatio 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/nexus/reference/pca_coda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis — pca_coda","text":"object LogRatio object. 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/nexus/reference/pca_coda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis — pca_coda","text":"dimensio::PCA object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Principal Components Analysis — pca_coda","text":"Aitchison, J. Greenacre, M. (2002). Biplots compositional data. Journal Royal Statistical Society: Series C (Applied Statistics), 51: 375-392. doi:10.1111/1467-9876.00275 . Filzmoser, P., Hron, K. Reimann, C. (2009). Principal component analysis compositional data outliers. Environmetrics, 20: 621-632. doi:10.1002/env.966 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis — pca_coda","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis — pca_coda","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Centered log-ratio clr <- transform_clr(coda)  ## PCA X <- pca(clr)  ## Plot viz_individuals(X)  viz_variables(X)"},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":null,"dir":"Reference","previous_headings":"","what":"Perturbation Operation — perturbation","title":"Perturbation Operation — perturbation","text":"Perturbation two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perturbation Operation — perturbation","text":"","code":"perturbation(x, y, ...)  # S4 method for numeric,numeric perturbation(x, y)  # S4 method for CompositionMatrix,numeric perturbation(x, y)  # S4 method for CompositionMatrix,matrix perturbation(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perturbation Operation — perturbation","text":"x, y numeric vector compositional data CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perturbation Operation — perturbation","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perturbation Operation — perturbation","text":"compositional geometry, perturbation plays role sum (translation). closed component-wise product two compositions.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perturbation Operation — perturbation","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perturbation Operation — perturbation","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Compositional Data — plot_coda","title":"Plot Compositional Data — plot_coda","text":"Displays compositional bar chart.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Compositional Data — plot_coda","text":"","code":"# S4 method for CompositionMatrix,missing plot(   x,   order = NULL,   decreasing = FALSE,   groups = get_groups(x),   horiz = TRUE,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   ... )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Compositional Data — plot_coda","text":"x CompositionMatrix object. order integer vector giving index column used ordering data. decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. horiz logical scalar. FALSE, bars drawn vertically first bar left. TRUE (default), bars drawn horizontally first bottom. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? ... parameters passed graphics::barplot().","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Compositional Data — plot_coda","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Compositional Data — plot_coda","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Compositional Data — plot_coda","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Bar plot plot(coda, order = 2)   ## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  plot(coda, order = 1, main = \"Kommos Ceramics\")  plot(coda, order = 1, horiz = FALSE, main = \"Kommos Ceramics\")"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Log-Ratios — plot_logratio","title":"Plot Log-Ratios — plot_logratio","text":"Displays density plot.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Log-Ratios — plot_logratio","text":"","code":"# S4 method for LogRatio,missing plot(   x,   order = NULL,   decreasing = FALSE,   groups = get_groups(x),   rug = TRUE,   ticksize = 0.05,   ncol = NULL,   flip = FALSE,   xlab = NULL,   ylab = NULL,   main = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   legend = list(x = \"topright\"),   ... )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Log-Ratios — plot_logratio","text":"x LogRatio object. order logical scalar: ratio ordered? decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. rug logical scalar: rug representation (1-d plot) data added plot? ticksize length-one numeric vector giving length ticks making rug. Positive lengths give inwards ticks. used rug TRUE. ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? xlab, ylab character vector giving x y axis labels. main character string giving main title plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed. ... graphical parameters, particularly, border col.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Log-Ratios — plot_logratio","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Log-Ratios — plot_logratio","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Log-Ratios — plot_logratio","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Log ratio clr <- transform_clr(coda) plot(clr, group = NULL, flip = TRUE, border = \"black\", col = NA)  plot(clr, flip = TRUE)"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Outliers — plot_outliers","title":"Plot Outliers — plot_outliers","text":"Plot Outliers","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Outliers — plot_outliers","text":"","code":"# S4 method for OutlierIndex,missing plot(   x,   qq = FALSE,   limit = TRUE,   probs = c(0.25, 0.75),   col = c(\"#004488\", \"#DDAA33\"),   pch = c(15, 16),   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Outliers — plot_outliers","text":"x OutlierIndex object. qq logical scalar: quantile-quantile plot produced? limit logical scalar: cut-value outlier detection displayed? used qq FALSE. probs length-two numeric vector representing probabilities. Corresponding quantile pairs define line drawn (see stats::qqline()). used qq TRUE. col vector colors. pch vector plotting character (symbol). xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Outliers — plot_outliers","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Outliers — plot_outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Outliers — plot_outliers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Outliers — plot_outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda) #>   ## Quantile-Quantile plot plot(out, qq = TRUE)   ## Plot plot(out)"},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":null,"dir":"Reference","previous_headings":"","what":"Powering Operation — powering","title":"Powering Operation — powering","text":"Perturbation two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Powering Operation — powering","text":"","code":"powering(x, a, ...)  # S4 method for numeric,numeric powering(x, a)  # S4 method for CompositionMatrix,numeric powering(x, a)"},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Powering Operation — powering","text":"x numeric vector compositional data CompositionMatrix object. numeric constant. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Powering Operation — powering","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Powering Operation — powering","text":"compositional geometry, powering replaces product vector scalar (scaling) defined closed powering components given scalar.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Powering Operation — powering","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Powering Operation — powering","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. arkhe remove_NA, remove_zero, replace_NA, replace_zero dimensio viz_individuals, viz_variables isopleuros ternary_plot","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Samples — samples","title":"Working With Samples — samples","text":"Retrieves defines sample names.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Samples — samples","text":"","code":"has_replicates(x)  get_samples(x)  set_samples(x) <- value  # S4 method for CompositionMatrix has_replicates(x)  # S4 method for LogRatio has_replicates(x)  # S4 method for OutlierIndex has_replicates(x)  # S4 method for CompositionMatrix get_samples(x)  # S4 method for LogRatio get_samples(x)  # S4 method for OutlierIndex get_samples(x)  # S4 method for CompositionMatrix set_samples(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Samples — samples","text":"x object get set samples. value possible value samples x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Samples — samples","text":"set_samples() returns object sort x new sample names assigned. get_samples() returns sample names x. has_replicates() returns logical scalar.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Samples — samples","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Samples — samples","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar Product — scalar","title":"Scalar Product — scalar","text":"Computes Aitchison scalar product two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar Product — scalar","text":"","code":"scalar(x, y, ...)  # S4 method for numeric,numeric scalar(x, y)  # S4 method for CompositionMatrix,CompositionMatrix scalar(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar Product — scalar","text":"x, y CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scalar Product — scalar","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scalar Product — scalar","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scalar Product — scalar","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":null,"dir":"Reference","previous_headings":"","what":"Metric Variance and Standard Deviation — spread","title":"Metric Variance and Standard Deviation — spread","text":"mvar() computes metric variance, .e. global measure spread. msd() computes metric standard deviation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metric Variance and Standard Deviation — spread","text":"","code":"mvar(x, ...)  msd(x, ...)  # S4 method for CompositionMatrix mvar(x)  # S4 method for CompositionMatrix msd(x)"},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metric Variance and Standard Deviation — spread","text":"x CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metric Variance and Standard Deviation — spread","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Metric Variance and Standard Deviation — spread","text":"Pawlowsky-Glahn, V. Egozcue, J. J. (2001). Geometric Approach Statistical Analysis Simplex. Stochastic Environmental Research Risk Assessment, 15(5): 384-398. doi:10.1007/s004770100077 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Metric Variance and Standard Deviation — spread","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metric Variance and Standard Deviation — spread","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827   ## Metric variance mvar(coda) #> [1] 1.691324  ## Metric standard deviation msd(coda) #> [1] 0.6502546"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object — subset","title":"Extract or Replace Parts of an Object — subset","text":"Operators acting objects extract replace parts.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object — subset","text":"","code":"# S4 method for CompositionMatrix,missing,missing,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,missing,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,missing,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,missing,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,index,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,index,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,index,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,index,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix [(x, i, j, ...) <- value  # S4 method for CompositionMatrix [[(x, i, j, ...) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object — subset","text":"x object extract element(s) replace element(s). , j Indices specifying elements extract replace. Indices numeric, integer character vectors empty (missing) NULL. Numeric values coerced integer .integer(). Character vectors matched name elements. empty index (comma separated blank) indicates entries dimension selected. ... Currently used. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement. value possible value element(s) x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object — subset","text":"subsetted object sort x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"subcomposition","dir":"Reference","previous_headings":"","what":"Subcomposition","title":"Extract or Replace Parts of an Object — subset","text":"drop FALSE, subsetting possible components CompositionMatrix object produce closed subcomposition (see examples).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object — subset","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of an Object — subset","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048  ## Subset coda[[1, 1]] # Get the first value #> [1] 0.488 coda[1] # Get the first value #> [1] 0.488 coda[, ] # Get all values #> <CompositionMatrix: 25 x 5> #>             A         B          C          D          E #> H1  0.4880000 0.3170000 0.03800000 0.06400000 0.09300000 #> H2  0.4820000 0.2380000 0.09000000 0.09200000 0.09800000 #> H3  0.3700000 0.0910000 0.34200000 0.09500000 0.10200000 #> H4  0.5090000 0.2380000 0.07200000 0.10100000 0.08000000 #> H5  0.4420000 0.3830000 0.02900000 0.07700000 0.06900000 #> H6  0.5230000 0.2620000 0.04200000 0.12500000 0.04800000 #> H7  0.4460000 0.3300000 0.04600000 0.12200000 0.05600000 #> H8  0.3460000 0.0520000 0.42900000 0.09600000 0.07700000 #> H9  0.4120000 0.1170000 0.26700000 0.09600000 0.10800000 #> H10 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000 #> H11 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000 #> H12 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000 #> H13 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000 #> H14 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000 #> H15 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000 #> H16 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000 #> H17 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000 #> H18 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306 #> H19 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000 #> H20 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000 #> H21 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000 #> H22 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000 #> H23 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000 #> H24 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000 #> H25 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000 coda[1, , drop = FALSE] # Get the first row #> <CompositionMatrix: 1 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093  ## Subcomposition subcoda <- coda[, 1:3, drop = FALSE] # Get the first three column head(subcoda) #> <CompositionMatrix: 6 x 3> #>            A         B          C #> H1 0.5788849 0.3760380 0.04507711 #> H2 0.5950617 0.2938272 0.11111111 #> H3 0.4607721 0.1133250 0.42590286 #> H4 0.6214896 0.2905983 0.08791209 #> H5 0.5175644 0.4484778 0.03395785 #> H6 0.6324063 0.3168077 0.05078597"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":null,"dir":"Reference","previous_headings":"","what":"Row Sums — totals","title":"Row Sums — totals","text":"Retrieves defines row sums (closure).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row Sums — totals","text":"","code":"get_totals(x)  set_totals(x) <- value  # S4 method for CompositionMatrix get_totals(x)  # S4 method for CompositionMatrix set_totals(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row Sums — totals","text":"x object get set totals. value possible value totals x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row Sums — totals","text":"set_totals() returns object sort x new row sums assigned. get_totals() returns row sums x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Row Sums — totals","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive Log-Ratios (ALR) — transform_alr","title":"Additive Log-Ratios (ALR) — transform_alr","text":"Computes ALR transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive Log-Ratios (ALR) — transform_alr","text":"","code":"transform_alr(object, ...)  # S4 method for CompositionMatrix transform_alr(object, j = ncol(object))"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive Log-Ratios (ALR) — transform_alr","text":"object CompositionMatrix object. ... Currently used. j integer giving index rationing part (denominator).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive Log-Ratios (ALR) — transform_alr","text":"ALR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Additive Log-Ratios (ALR) — transform_alr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additive Log-Ratios (ALR) — transform_alr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additive Log-Ratios (ALR) — transform_alr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered Log-Ratios (CLR) — transform_clr","title":"Centered Log-Ratios (CLR) — transform_clr","text":"Computes CLR transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered Log-Ratios (CLR) — transform_clr","text":"","code":"transform_clr(object, ...)  # S4 method for CompositionMatrix transform_clr(object, weights = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Centered Log-Ratios (CLR) — transform_clr","text":"object CompositionMatrix object. ... Currently used. weights logical scalar: sould varying weight used. FALSE (default), equally-weighted parts used. Alternatively, positive numeric vector weights can specified.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Centered Log-Ratios (CLR) — transform_clr","text":"CLR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Centered Log-Ratios (CLR) — transform_clr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Centered Log-Ratios (CLR) — transform_clr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Centered Log-Ratios (CLR) — transform_clr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Isometric Log-Ratios (ILR) — transform_ilr","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"Computes ILR transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"","code":"transform_ilr(object, ...)  # S4 method for CompositionMatrix transform_ilr(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"ILR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"ILR transformation provides coordinates composition respect given orthonormal basis. transform_ilr() uses orthonormal basis (Helmert matrix) originally defined Egozcue et al. (2003).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Log-Ratio Transformation — transform_inverse","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"Computes inverse log-ratio transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"","code":"transform_inverse(object, ...)  # S4 method for CLR transform_inverse(object)  # S4 method for ALR transform_inverse(object)  # S4 method for ILR transform_inverse(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Fišerová, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455‑468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise Log-Ratios (LR) — transform_lr","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"Computes pairwise log-ratio transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"","code":"transform_lr(object, ...)  # S4 method for CompositionMatrix transform_lr(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"LR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot Log-Ratios (PLR) — transform_plr","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"Computes PLR transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"","code":"transform_plr(object, ...)  # S4 method for CompositionMatrix transform_plr(object, pivot = 1)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"object CompositionMatrix object. ... Currently used. pivot integer giving index pivotal variable.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"PLR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"Fišerová, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455‑468. doi:10.1007/s11004-011-9333-x . Hron, K., Filzmoser, P., de Caritat, P., Fišerová, E. & Gardlo, . (2017). Weighted Pivot Coordinates Compositional Data Application Geochemical Mapping. Mathematical Geosciences, 49(6), 797-814. doi:10.1007/s11004-017-9684-z . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":null,"dir":"Reference","previous_headings":"","what":"Variation Matrix — variation","title":"Variation Matrix — variation","text":"Computes variation matrix (Aitchison 1986, definition 4.4).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variation Matrix — variation","text":"","code":"variation(x, ...)  # S4 method for CompositionMatrix variation(x)"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variation Matrix — variation","text":"x CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variation Matrix — variation","text":"matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variation Matrix — variation","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variation Matrix — variation","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variation Matrix — variation","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Variation matrix ## (Aitchison 1986, definition 4.4) variation(coda) #>            A         B         C          D         E #> A 0.00000000 0.2592742 1.5328586 0.08281464 0.1385604 #> B 0.25927416 0.0000000 3.0006804 0.54727146 0.6490135 #> C 1.53285862 3.0006804 0.0000000 1.11145146 0.9476375 #> D 0.08281464 0.5472715 1.1114515 0.00000000 0.1870579 #> E 0.13856035 0.6490135 0.9476375 0.18705791 0.0000000"}]
+[{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"get-started","dir":"Articles","previous_headings":"","what":"Get started","title":"Introduction to nexus","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e. quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986; Greenacre 2021). assumes keep data tidy: variable must saved column observation (sample) must saved row. class simple use inherits base matrix: CompositionMatrix represents closed composition matrix: row matrix sum 1 (relative changes relevant compositional data analysis). original row sums kept internally, source data can restored:","code":"library(nexus) ## Mineral compositions of rock specimens data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Coerce to count data counts <- as_amounts(coda)  all.equal(hongite, as.data.frame(counts)) #> [1] TRUE ## Compositional bar plot plot(coda, order = \"A\")"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-reference-groups","dir":"Articles","previous_headings":"Get started","what":"Working with (reference) groups","title":"Introduction to nexus","text":"Provenance studies typically rely two approaches, can used together: Identification groups among artifacts studied, based mineralogical geochemical criteria (clustering). Comparison -called reference groups, .e. known geological sources productive contexts (classification). nexus allows specify whether observation belongs specific group (). coercing data.frame CompositionMatrix object, attempt made automatically detect groups mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used groups argument as_composition(). get_groups(x) set_groups(x) <- value allow retrieve set groups existing CompositionMatrix (NA can used specify sample belong group): groups defined, can used methods (e.g. plotting).","code":"## Create a data.frame X <- data.frame(   groups = c(\"A\", \"A\", \"B\", \"A\", \"B\", \"C\", \"C\", \"C\", \"B\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) has_groups(Y) #> [1] TRUE ## Set groups (NA means no group) set_groups(Y) <- c(\"X\", \"X\", \"Y\", \"X\", \"Y\", NA, NA, NA, \"Y\")  ## Retrieve groups get_groups(Y) #> [1] \"X\" \"X\" \"Y\" \"X\" \"Y\" NA  NA  NA  \"Y\""},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-repeated-measurements","dir":"Articles","previous_headings":"Get started","what":"Working with repeated measurements","title":"Introduction to nexus","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. coercing data.frame CompositionMatrix object, attempt made automatically detect samples mapping column names. matching column found, row names used default. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used samples argument as_composition(). get_samples(x) set_samples(x) <- value allow retrieve set sample names existing CompositionMatrix (missing values allowed): Note presence repeated measurements may affect calculations (read documentation carefully).","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) has_replicates(Y) #> [1] TRUE ## Set sample names set_samples(Y) <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\")  ## Retrieve groups get_samples(Y) #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\""},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"log-ratio-transformations","dir":"Articles","previous_headings":"Get started","what":"Log-ratio transformations","title":"Introduction to nexus","text":"amount every part limited whole. absolute amount whole noninformative since typically due artifacts measurement procedure. Thus relative changes relevant. relative amount one part increases, amounts parts must decrease, introducing spurious anticorrelation.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"ceramic-compositions","dir":"Articles","previous_headings":"Use cases","what":"Ceramic compositions","title":"Introduction to nexus","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\")  ## Remove rows with missing values kommos <- remove_NA(kommos, margin = 1)  ## Coerce to a compositional matrix coda <- as_composition(kommos) #> 2 qualitative variables were removed: type, date.  ## Set groups set_groups(coda) <- kommos$type ## Compositional bar plot plot(coda, order = \"Ca\") ## CLR clr <- transform_clr(coda) plot(clr, border = c(\"#EE7733\", \"#0077BB\", \"#33BBEE\", \"#EE3377\")) ## PCA clr_pca <- pca(clr, scale = FALSE)  viz_individuals(clr_pca, highlight = get_groups(coda), pch = 16,                  col = c(\"#EE7733\", \"#0077BB\", \"#33BBEE\", \"#EE3377\")) viz_variables(clr_pca)"},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/nexus/index.html","id":"nexus-","dir":"","previous_headings":"","what":"Sourcing Archaeological Materials by Chemical Composition","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Exploration analysis compositional data framework Aitchison (1986). package provides tools chemical fingerprinting source tracking ancient materials. Initial development progress.","code":"To cite nexus in publications use:    Frerebeau N (2023). _nexus: Sourcing Archaeological Materials by   Chemical Composition_. Université Bordeaux Montaigne, Pessac, France.   R package version 0.0.0.9000, <https://packages.tesselle.org/nexus/>.  Une entrée BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{nexus: Sourcing Archaeological Materials by Chemical Composition}},     year = {2023},     organization = {Université Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.0.0.9000},     url = {https://packages.tesselle.org/nexus/},   }  This package is a part of the tesselle project <https://www.tesselle.org>."},{"path":"https://packages.tesselle.org/nexus/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Sourcing Archaeological Materials by Chemical Composition","text":"can install released version nexus CRAN : development version GitHub :","code":"install.packages(\"nexus\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/nexus\")"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Sourcing Archaeological Materials by Chemical Composition","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e. quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986). assumes keep data tidy: variable must saved column observation (sample) must saved row. new classes simple use inherit base matrix: CompositionMatrix class special slots (see vignette(\"nexus\")): samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE).","code":"## Load the package library(nexus) ## Mineral compositions of rock specimens data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Get samples get_samples(Y) #> [1] \"A\" \"A\" \"A\" \"B\" \"B\" \"B\" \"C\" \"C\" \"C\"  ## Get groups get_groups(Y) #> [1] \"X\" \"X\" \"X\" \"X\" \"X\" \"X\" \"Y\" \"Y\" \"Y\""},{"path":"https://packages.tesselle.org/nexus/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Please note nexus project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Matrix — CompositionMatrix-class","title":"Compositional Matrix — CompositionMatrix-class","text":"S4 class represent compositional data.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Compositional Matrix — CompositionMatrix-class","text":"totals numeric vector store absolute row sums (closure compositions). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names ().","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compositional Matrix — CompositionMatrix-class","text":"class inherits matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Compositional Matrix — CompositionMatrix-class","text":"code snippets , x CompositionMatrix object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Matrix — CompositionMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Matrix — CompositionMatrix-class","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## codaccess dim(coda) # Get the matrix dimensions #> [1] 25  5 row(coda) # Get the row indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,]    1    1    1    1    1 #>  [2,]    2    2    2    2    2 #>  [3,]    3    3    3    3    3 #>  [4,]    4    4    4    4    4 #>  [5,]    5    5    5    5    5 #>  [6,]    6    6    6    6    6 #>  [7,]    7    7    7    7    7 #>  [8,]    8    8    8    8    8 #>  [9,]    9    9    9    9    9 #> [10,]   10   10   10   10   10 #> [11,]   11   11   11   11   11 #> [12,]   12   12   12   12   12 #> [13,]   13   13   13   13   13 #> [14,]   14   14   14   14   14 #> [15,]   15   15   15   15   15 #> [16,]   16   16   16   16   16 #> [17,]   17   17   17   17   17 #> [18,]   18   18   18   18   18 #> [19,]   19   19   19   19   19 #> [20,]   20   20   20   20   20 #> [21,]   21   21   21   21   21 #> [22,]   22   22   22   22   22 #> [23,]   23   23   23   23   23 #> [24,]   24   24   24   24   24 #> [25,]   25   25   25   25   25 col(coda, as.factor = TRUE) # Get the column indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,] A    B    C    D    E    #>  [2,] A    B    C    D    E    #>  [3,] A    B    C    D    E    #>  [4,] A    B    C    D    E    #>  [5,] A    B    C    D    E    #>  [6,] A    B    C    D    E    #>  [7,] A    B    C    D    E    #>  [8,] A    B    C    D    E    #>  [9,] A    B    C    D    E    #> [10,] A    B    C    D    E    #> [11,] A    B    C    D    E    #> [12,] A    B    C    D    E    #> [13,] A    B    C    D    E    #> [14,] A    B    C    D    E    #> [15,] A    B    C    D    E    #> [16,] A    B    C    D    E    #> [17,] A    B    C    D    E    #> [18,] A    B    C    D    E    #> [19,] A    B    C    D    E    #> [20,] A    B    C    D    E    #> [21,] A    B    C    D    E    #> [22,] A    B    C    D    E    #> [23,] A    B    C    D    E    #> [24,] A    B    C    D    E    #> [25,] A    B    C    D    E    #> Levels: A B C D E nrow(coda) # Get the number of rows #> [1] 25 ncol(coda) # Get the number of columns #> [1] 5 dimnames(coda) # Get the dimension names #> [[1]] #>  [1] \"H1\"  \"H2\"  \"H3\"  \"H4\"  \"H5\"  \"H6\"  \"H7\"  \"H8\"  \"H9\"  \"H10\" \"H11\" \"H12\" #> [13] \"H13\" \"H14\" \"H15\" \"H16\" \"H17\" \"H18\" \"H19\" \"H20\" \"H21\" \"H22\" \"H23\" \"H24\" #> [25] \"H25\" #>  #> [[2]] #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" #>  rownames(coda) <- LETTERS[1:25] # Set the row names rownames(coda) # Get the rownames #>  [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" colnames(coda) <- letters[21:25] # Set the column names colnames(coda) # Get the column names #> [1] \"u\" \"v\" \"w\" \"x\" \"y\""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Ratio Matrix — LogRatio-class","title":"Log-Ratio Matrix — LogRatio-class","text":"S4 classes represent log-ratio data transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Log-Ratio Matrix — LogRatio-class","text":"totals numeric vector store absolute row sums (closure compositions). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). parts character vector store part names. ratio character vector store ratio names. order integer vector store original ordering columns. base numeric matrix store basis transformation. weights numeric vector store weights assigned respective log-ratios.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log-Ratio Matrix — LogRatio-class","text":"classes inherit matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Log-Ratio Matrix — LogRatio-class","text":"code snippets , x LogRatio object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Log-Ratio Matrix — LogRatio-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical Matrix — LogicalMatrix-class","title":"Logical Matrix — LogicalMatrix-class","text":"S4 classes represent \\(m \\times p\\) logical matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Logical Matrix — LogicalMatrix-class","text":".Data \\(m \\times p\\) logical matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logical Matrix — LogicalMatrix-class","text":"class inherits matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Logical Matrix — LogicalMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Matrix — NumericMatrix-class","title":"Numeric Matrix — NumericMatrix-class","text":"S4 classes represent \\(m \\times p\\) numeric matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Numeric Matrix — NumericMatrix-class","text":".Data \\(m \\times p\\) numeric matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Numeric Matrix — NumericMatrix-class","text":"class inherits matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Numeric Matrix — NumericMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Outliers — OutlierIndex-class","title":"Outliers — OutlierIndex-class","text":"S4 class store result outlier detection.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Outliers — OutlierIndex-class","text":".Data logical vector. samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). distances numeric vector giving squared Mahalanobis distance. limit numeric value giving cut-value used outlier detection (quantile Chi-squared distribution). robust logical scalar: robust estimators used? dof (non-negative) numeric value giving degrees freedom.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outliers — OutlierIndex-class","text":"classes inherit logical.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Outliers — OutlierIndex-class","text":"code snippets , x OutlierIndex object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outliers — OutlierIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Summary Statistics of Data Subsets — aggregate","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"Splits data subsets, computes summary statistics , returns result.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"","code":"# S4 method for CompositionMatrix aggregate(x, by, FUN, ...)"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"x CompositionMatrix object. character string specifying grouping element. must one \"samples\" \"groups\". unambiguous substring can given. FUN function compute summary statistics. ... arguments passed FUN.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Compositional mean by sample aggregate(Y, by = \"samples\", FUN = mean) #>          Ca        Fe         Na #> A 0.4468157 0.4539051 0.09927924 #> B 0.4746506 0.4742013 0.05114805 #> C 0.3067545 0.6238351 0.06941036  ## Compositional mean by group aggregate(Y, by = \"groups\", FUN = mean) #>          Ca        Fe         Na #> X 0.4625002 0.4659342 0.07156562 #> Y 0.3067545 0.6238351 0.06941036"},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":null,"dir":"Reference","previous_headings":"","what":"Operations in the Simplex — arithmetic","title":"Operations in the Simplex — arithmetic","text":"Operators performing operations simplex.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Operations in the Simplex — arithmetic","text":"","code":"# S4 method for CompositionMatrix,CompositionMatrix +(e1, e2)  # S4 method for CompositionMatrix,CompositionMatrix -(e1, e2)  # S4 method for CompositionMatrix,numeric *(e1, e2)  # S4 method for numeric,CompositionMatrix *(e1, e2)"},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Operations in the Simplex — arithmetic","text":"e1 CompositionMatrix object. e2 CompositionMatrix object numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Operations in the Simplex — arithmetic","text":"CompositionMatrix object numeric vector (e1).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Operations in the Simplex — arithmetic","text":"+ Perturbation operation. - Perturbation operation. * Powering operation.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Operations in the Simplex — arithmetic","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Operations in the Simplex — arithmetic","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Amounts — as_amounts","title":"Coerce to Amounts — as_amounts","text":"Coerce Amounts","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Amounts — as_amounts","text":"","code":"as_amounts(from, ...)  # S4 method for CompositionMatrix as_amounts(from)"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Amounts — as_amounts","text":"CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Amounts — as_amounts","text":"numeric  matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Amounts — as_amounts","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Amounts — as_amounts","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 200 215 271 289 272 265 227 276 217 162 297 204 340 170 221 224 176 306 258 #> [20] 242  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>           V1        V2        V3         V4         V5 samples groups #> S1 0.0850000 0.3900000 0.1950000 0.24500000 0.08500000      S1   <NA> #> S2 0.2279070 0.1627907 0.2697674 0.01395349 0.32558140      S2   <NA> #> S3 0.2730627 0.3653137 0.1033210 0.18450185 0.07380074      S3   <NA> #> S4 0.2387543 0.2179931 0.2698962 0.18685121 0.08650519      S4   <NA> #> S5 0.1654412 0.2683824 0.1948529 0.15073529 0.22058824      S5   <NA> #> S6 0.1622642 0.2188679 0.1433962 0.16226415 0.31320755      S6   <NA>"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Closed Compositional Matrix — as_composition","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"Coerces object CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"","code":"as_composition(from, ...)  # S4 method for numeric as_composition(from)  # S4 method for matrix as_composition(from)  # S4 method for data.frame as_composition(   from,   samples = NULL,   groups = NULL,   verbose = getOption(\"nexus.verbose\") )"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"matrix data.frame coerced. ... Currently used. samples integer giving index column used sample identification: allows identify replicated measurements. NULL (default), row names used sample IDs. groups integer giving index column used group samples. NULL (default), grouping stored. verbose logical scalar: R report extra information progress?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"CompositionMatrix class special slots: samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names (case insensitive, plural insensitive). behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying columns used. See vignette(\"nexus\").","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"non-numeric variable removed.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 259 143 249 178 276 201 390 235 356 336  96 239 337 257 216 234 224 222 310 #> [20] 292  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>            V1         V2         V3        V4         V5 samples groups #> S1 0.06177606 0.06177606 0.35135135 0.3281853 0.19691120      S1   <NA> #> S2 0.04895105 0.04195804 0.39160839 0.4615385 0.05594406      S2   <NA> #> S3 0.40160643 0.14056225 0.02409639 0.3052209 0.12851406      S3   <NA> #> S4 0.07303371 0.16292135 0.27528090 0.1123596 0.37640449      S4   <NA> #> S5 0.12318841 0.24637681 0.21376812 0.1630435 0.25362319      S5   <NA> #> S6 0.06467662 0.29353234 0.30845771 0.1442786 0.18905473      S6   <NA>"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph of Log-ratios — as_graph","title":"Graph of Log-ratios — as_graph","text":"Produces graph log-ratios.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph of Log-ratios — as_graph","text":"","code":"as_graph(object, ...)  # S4 method for LR as_graph(object)  # S4 method for ALR as_graph(object)  # S4 method for ILR as_graph(object)"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph of Log-ratios — as_graph","text":"object LogRatio object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph of Log-ratios — as_graph","text":"igraph graph object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graph of Log-ratios — as_graph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph of Log-ratios — as_graph","text":"","code":"library(igraph) #>  #> Attaching package: ‘igraph’ #> The following objects are masked from ‘package:stats’: #>  #>     decompose, spectrum #> The following object is masked from ‘package:base’: #>  #>     union  ## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda) lr_graph <- as_graph(lr) plot(lr_graph)   ## Additive log-ratio alr <- transform_alr(coda) alr_graph <- as_graph(alr) plot(alr_graph)   ## Isometric log-ratio ilr <- transform_ilr(coda) ilr_graph <- as_graph(ilr) plot(ilr_graph)   plr <- transform_plr(coda) plr_graph <- as_graph(plr) plot(plr_graph)"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":null,"dir":"Reference","previous_headings":"","what":"Closure Operation — closure","title":"Closure Operation — closure","text":"Closes compositions sum 1.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Closure Operation — closure","text":"","code":"closure(x, ...)  # S4 method for numeric closure(x, total = 1, na.rm = FALSE)  # S4 method for matrix closure(x, total = 1, na.rm = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Closure Operation — closure","text":"x numeric vector matrix. ... Currently used. total numeric vector specifying total amount compositions closed (defaults 1). na.rm logical scalar: missing values removed?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Closure Operation — closure","text":"numeric vector matrix (x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Closure Operation — closure","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Closure Operation — closure","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":null,"dir":"Reference","previous_headings":"","what":"Covariance Matrix — covariance","title":"Covariance Matrix — covariance","text":"Computes (centered) log-ratio covariance matrix (see ).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Covariance Matrix — covariance","text":"","code":"covariance(x, ...)  # S4 method for CompositionMatrix covariance(x, center = FALSE, method = \"pearson\")  # S4 method for ALR covariance(x, method = \"pearson\")  # S4 method for CLR covariance(x, method = \"pearson\")"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Covariance Matrix — covariance","text":"x CompositionMatrix object. ... Currently used. center logical scalar: centered log-ratio covariance matrix computed? method character string indicating covariance computed (see stats::cov()).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Covariance Matrix — covariance","text":"matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Covariance Matrix — covariance","text":"covariance(ALR): Computes log-ratio covariance matrix (Aitchison 1986, definition 4.5). covariance(CLR): Computes centered log-ratio covariance matrix (Aitchison 1986, definition 4.6).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Covariance Matrix — covariance","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Covariance Matrix — covariance","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Covariance Matrix — covariance","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Log-ratio covariance matrix ## (Aitchison 1986, definition 4.5) covariance(coda, center = FALSE) #>            A_E        B_E        C_E       D_E #> A_E  0.1385604  0.2641498 -0.2233304 0.1214018 #> B_E  0.2641498  0.6490135 -0.7020147 0.1444000 #> C_E -0.2233304 -0.7020147  0.9476375 0.0116220 #> D_E  0.1214018  0.1444000  0.0116220 0.1870579  ## Centered log-ratio covariance matrix ## (Aitchison 1986, definition 4.6) covariance(coda, center = TRUE) #>             A           B           C           D           E #> A  0.06443676  0.17907284 -0.24408053  0.01453821 -0.01396727 #> B  0.17907284  0.55298309 -0.73371823  0.02658296 -0.02492066 #> C -0.24408053 -0.73371823  0.98026080 -0.04186819  0.03940616 #> D  0.01453821  0.02658296 -0.04186819  0.04745430 -0.04670728 #> E -0.01396727 -0.02492066  0.03940616 -0.04670728  0.04618906"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Distances — dist","title":"Distances — dist","text":"Computes log-ratio variance matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distances — dist","text":"","code":"# S4 method for CompositionMatrix dist(x, method = \"euclidean\", diag = FALSE, upper = FALSE, p = 2)"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distances — dist","text":"x CompositionMatrix object. method character string specifying distance measure used. See stats::dist() available distances. diag logical scalar indicating whether diagonal distance matrix printed. upper logical scalar indicating whether upper triangle distance matrix printed. p integer giving power Minkowski distance.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distances — dist","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distances — dist","text":"Distances computed CLR-transformed data.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Distances — dist","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distances — dist","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distances — dist","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Aitchison distance ## (euclidean distance between CLR-transformed compositions) d <- dist(coda)  ## Cluster dendrogram h <- hclust(d, method = \"ward.D2\") plot(h)"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Groups — groups","title":"Working With Groups — groups","text":"Retrieves defines groups observations belong.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Groups — groups","text":"","code":"has_groups(x)  get_groups(x)  set_groups(x) <- value  # S4 method for CompositionMatrix has_groups(x)  # S4 method for LogRatio has_groups(x)  # S4 method for OutlierIndex has_groups(x)  # S4 method for CompositionMatrix get_groups(x)  # S4 method for LogRatio get_groups(x)  # S4 method for OutlierIndex get_groups(x)  # S4 method for CompositionMatrix set_groups(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Groups — groups","text":"x object get set groups. value possible value groups x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Groups — groups","text":"set_groups() returns object sort x new group names assigned. get_groups() returns group names x. has_groups() returns logical scalar.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Groups — groups","text":"See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Groups — groups","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":null,"dir":"Reference","previous_headings":"","what":"Hongite Mineralogy — hongite","title":"Hongite Mineralogy — hongite","text":"Mineral compositions 25 rock specimens hongite type.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hongite Mineralogy — hongite","text":"","code":"hongite"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hongite Mineralogy — hongite","text":"data.frame 5 variables (minerals): Albite (percent). B Blandite (percent). C Cornite (percent). D Daubite (percent). E Endite (percent).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hongite Mineralogy — hongite","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":null,"dir":"Reference","previous_headings":"","what":"Mahalanobis Distance — mahalanobis","title":"Mahalanobis Distance — mahalanobis","text":"Computes squared Mahalanobis distance rows x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mahalanobis Distance — mahalanobis","text":"","code":"# S4 method for CompositionMatrix mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))  # S4 method for ILR mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mahalanobis Distance — mahalanobis","text":"x CompositionMatrix ILR object. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mahalanobis Distance — mahalanobis","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mahalanobis Distance — mahalanobis","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mahalanobis Distance — mahalanobis","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Mahalanobis distance mahalanobis(coda) #>  #>         H1         H2         H3         H4         H5         H6         H7  #>  5.4655057  0.4098836  2.2782942  1.3153083 15.7930890  5.9302032 32.6554433  #>         H8         H9        H10        H11        H12        H13        H14  #> 24.7487338  1.3415634 25.3311592  1.8975808  6.2480419  4.5679191 20.6506579  #>        H15        H16        H17        H18        H19        H20        H21  #>  1.7973233 20.7950173  8.1328081  1.3600287  2.5432737  4.5402709  2.3044368  #>        H22        H23        H24        H25  #>  1.4282370 10.0877414  3.6234908  6.7280891"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Mean — mean","title":"Compositional Mean — mean","text":"Compositional Mean","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compositional Mean — mean","text":"","code":"# S4 method for CompositionMatrix mean(x, na.rm = FALSE, ...)"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compositional Mean — mean","text":"x CompositionMatrix object. na.rm logical scalar: missing values removed? ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compositional Mean — mean","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compositional Mean — mean","text":"Closed vector columns geometric means.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compositional Mean — mean","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Mean — mean","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Mean — mean","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827   ## Metric variance mvar(coda) #> [1] 1.691324  ## Metric standard deviation msd(coda) #> [1] 0.6502546"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"Exploration analysis compositional data framework Aitchison (1986) <isbn: 9780412280603>. package provides tools chemical fingerprinting source tracking ancient materials.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"nexus uses following options() configure behavior: nexus.autodetect: logical scalar. Try automatically assign values corresponding slot CompositionMatrix object coercing data.frame? Defaults TRUE. nexus.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-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/nexus/reference/outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection — outliers","title":"Outlier Detection — outliers","text":"Outlier Detection","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection — outliers","text":"","code":"outliers(object, ...)  # S4 method for CompositionMatrix outliers(   object,   center = NULL,   cov = NULL,   robust = TRUE,   method = c(\"mve\", \"mcd\"),   quantile = 0.975,   ... )"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection — outliers","text":"object CompositionMatrix. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE. quantile length-one numeric vector giving significance level. quantile used cut-value outlier detection: observations larger (squared) Mahalanobis distance considered potential outliers.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection — outliers","text":"OutlierIndex object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Outlier Detection — outliers","text":"outlier can defined large Mahalanobis distance observations. way, certain proportion observations can identified, e.g. top 2% values (.e. values 0.98th percentile Chi-2 distribution). one hand, Mahalanobis distance likely strongly affected presence outliers. Rousseeuw van Zomeren (1990) thus recommend using robust methods (excessively affected presence outliers). hand, choice threshold classifying observation outlier discussed. apparent reason particular threshold applicable data sets (Filzmoser, Garrett, Reimann 2005).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Outlier Detection — outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 . Rousseeuw, P. J. & van Zomeren, B. C. (1990). Unmasking Multivariate Outliers Leverage Points. Journal American Statistical Association, 85(411): 633-639. doi:10.1080/01621459.1990.10474920 . Santos, F. (2020). Modern methods old data: overview robust methods outliers detection applications osteology. Journal Archaeological Science: Reports, 32, 102423. doi:10.1016/j.jasrep.2020.102423 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outlier Detection — outliers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection — outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda) #>   ## Quantile-Quantile plot plot(out, qq = TRUE)   ## Plot plot(out)"},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis — pca_coda","title":"Principal Components Analysis — pca_coda","text":"Computes principal components analysis based singular value decomposition.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis — pca_coda","text":"","code":"# S4 method for CompositionMatrix pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for LogRatio 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/nexus/reference/pca_coda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis — pca_coda","text":"object LogRatio object. 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/nexus/reference/pca_coda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis — pca_coda","text":"dimensio::PCA object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Principal Components Analysis — pca_coda","text":"Aitchison, J. Greenacre, M. (2002). Biplots compositional data. Journal Royal Statistical Society: Series C (Applied Statistics), 51: 375-392. doi:10.1111/1467-9876.00275 . Filzmoser, P., Hron, K. Reimann, C. (2009). Principal component analysis compositional data outliers. Environmetrics, 20: 621-632. doi:10.1002/env.966 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis — pca_coda","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis — pca_coda","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Centered log-ratio clr <- transform_clr(coda)  ## PCA X <- pca(clr)  ## Plot viz_individuals(X)  viz_variables(X)"},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":null,"dir":"Reference","previous_headings":"","what":"Perturbation Operation — perturbation","title":"Perturbation Operation — perturbation","text":"Perturbation two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perturbation Operation — perturbation","text":"","code":"perturbation(x, y, ...)  # S4 method for numeric,numeric perturbation(x, y)  # S4 method for CompositionMatrix,numeric perturbation(x, y)  # S4 method for CompositionMatrix,matrix perturbation(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perturbation Operation — perturbation","text":"x, y numeric vector compositional data CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perturbation Operation — perturbation","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perturbation Operation — perturbation","text":"compositional geometry, perturbation plays role sum (translation). closed component-wise product two compositions.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perturbation Operation — perturbation","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perturbation Operation — perturbation","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Compositional Data — plot_coda","title":"Plot Compositional Data — plot_coda","text":"Displays compositional bar chart.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Compositional Data — plot_coda","text":"","code":"# S4 method for CompositionMatrix,missing plot(   x,   order = NULL,   decreasing = FALSE,   groups = get_groups(x),   horiz = TRUE,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   ... )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Compositional Data — plot_coda","text":"x CompositionMatrix object. order integer vector giving index column used ordering data. decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. horiz logical scalar. FALSE, bars drawn vertically first bar left. TRUE (default), bars drawn horizontally first bottom. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? ... parameters passed graphics::barplot().","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Compositional Data — plot_coda","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Compositional Data — plot_coda","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Compositional Data — plot_coda","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Bar plot plot(coda, order = 2)   ## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  plot(coda, order = 1, main = \"Kommos Ceramics\")  plot(coda, order = 1, horiz = FALSE, main = \"Kommos Ceramics\")"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Log-Ratios — plot_logratio","title":"Plot Log-Ratios — plot_logratio","text":"Displays density plot.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Log-Ratios — plot_logratio","text":"","code":"# S4 method for LogRatio,missing plot(   x,   order = NULL,   decreasing = FALSE,   groups = get_groups(x),   rug = TRUE,   ticksize = 0.05,   ncol = NULL,   flip = FALSE,   xlab = NULL,   ylab = NULL,   main = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   legend = list(x = \"topright\"),   ... )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Log-Ratios — plot_logratio","text":"x LogRatio object. order logical scalar: ratio ordered? decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. rug logical scalar: rug representation (1-d plot) data added plot? ticksize length-one numeric vector giving length ticks making rug. Positive lengths give inwards ticks. used rug TRUE. ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? xlab, ylab character vector giving x y axis labels. main character string giving main title plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed. ... graphical parameters, particularly, border col.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Log-Ratios — plot_logratio","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Log-Ratios — plot_logratio","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Log-Ratios — plot_logratio","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Log ratio clr <- transform_clr(coda) plot(clr, group = NULL, flip = TRUE, border = \"black\", col = NA)  plot(clr, flip = TRUE)"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Outliers — plot_outliers","title":"Plot Outliers — plot_outliers","text":"Plot Outliers","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Outliers — plot_outliers","text":"","code":"# S4 method for OutlierIndex,missing plot(   x,   qq = FALSE,   limit = TRUE,   probs = c(0.25, 0.75),   col = c(\"#004488\", \"#DDAA33\"),   pch = c(15, 16),   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Outliers — plot_outliers","text":"x OutlierIndex object. qq logical scalar: quantile-quantile plot produced? limit logical scalar: cut-value outlier detection displayed? used qq FALSE. probs length-two numeric vector representing probabilities. Corresponding quantile pairs define line drawn (see stats::qqline()). used qq TRUE. col vector colors. pch vector plotting character (symbol). xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Outliers — plot_outliers","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Outliers — plot_outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Outliers — plot_outliers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Outliers — plot_outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda) #>   ## Quantile-Quantile plot plot(out, qq = TRUE)   ## Plot plot(out)"},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":null,"dir":"Reference","previous_headings":"","what":"Powering Operation — powering","title":"Powering Operation — powering","text":"Perturbation two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Powering Operation — powering","text":"","code":"powering(x, a, ...)  # S4 method for numeric,numeric powering(x, a)  # S4 method for CompositionMatrix,numeric powering(x, a)"},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Powering Operation — powering","text":"x numeric vector compositional data CompositionMatrix object. numeric constant. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Powering Operation — powering","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Powering Operation — powering","text":"compositional geometry, powering replaces product vector scalar (scaling) defined closed powering components given scalar.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Powering Operation — powering","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Powering Operation — powering","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. arkhe remove_NA, remove_zero, replace_NA, replace_zero dimensio viz_individuals, viz_variables isopleuros ternary_plot","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Samples — samples","title":"Working With Samples — samples","text":"Retrieves defines sample names.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Samples — samples","text":"","code":"has_replicates(x)  get_samples(x)  set_samples(x) <- value  # S4 method for CompositionMatrix has_replicates(x)  # S4 method for LogRatio has_replicates(x)  # S4 method for OutlierIndex has_replicates(x)  # S4 method for CompositionMatrix get_samples(x)  # S4 method for LogRatio get_samples(x)  # S4 method for OutlierIndex get_samples(x)  # S4 method for CompositionMatrix set_samples(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Samples — samples","text":"x object get set samples. value possible value samples x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Samples — samples","text":"set_samples() returns object sort x new sample names assigned. get_samples() returns sample names x. has_replicates() returns logical scalar.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Samples — samples","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Samples — samples","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar Product — scalar","title":"Scalar Product — scalar","text":"Computes Aitchison scalar product two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar Product — scalar","text":"","code":"scalar(x, y, ...)  # S4 method for numeric,numeric scalar(x, y)  # S4 method for CompositionMatrix,CompositionMatrix scalar(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar Product — scalar","text":"x, y CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scalar Product — scalar","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scalar Product — scalar","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scalar Product — scalar","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":null,"dir":"Reference","previous_headings":"","what":"Metric Variance and Standard Deviation — spread","title":"Metric Variance and Standard Deviation — spread","text":"mvar() computes metric variance, .e. global measure spread. msd() computes metric standard deviation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metric Variance and Standard Deviation — spread","text":"","code":"mvar(x, ...)  msd(x, ...)  # S4 method for CompositionMatrix mvar(x)  # S4 method for CompositionMatrix msd(x)"},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metric Variance and Standard Deviation — spread","text":"x CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metric Variance and Standard Deviation — spread","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Metric Variance and Standard Deviation — spread","text":"Pawlowsky-Glahn, V. Egozcue, J. J. (2001). Geometric Approach Statistical Analysis Simplex. Stochastic Environmental Research Risk Assessment, 15(5): 384-398. doi:10.1007/s004770100077 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Metric Variance and Standard Deviation — spread","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/spread.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metric Variance and Standard Deviation — spread","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827   ## Metric variance mvar(coda) #> [1] 1.691324  ## Metric standard deviation msd(coda) #> [1] 0.6502546"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object — subset","title":"Extract or Replace Parts of an Object — subset","text":"Operators acting objects extract replace parts.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object — subset","text":"","code":"# S4 method for CompositionMatrix,missing,missing,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,missing,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,missing,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,missing,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,index,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,index,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,index,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,index,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix [(x, i, j, ...) <- value  # S4 method for CompositionMatrix [[(x, i, j, ...) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object — subset","text":"x object extract element(s) replace element(s). , j Indices specifying elements extract replace. Indices numeric, integer character vectors empty (missing) NULL. Numeric values coerced integer .integer(). Character vectors matched name elements. empty index (comma separated blank) indicates entries dimension selected. ... Currently used. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement. value possible value element(s) x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object — subset","text":"subsetted object sort x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"subcomposition","dir":"Reference","previous_headings":"","what":"Subcomposition","title":"Extract or Replace Parts of an Object — subset","text":"drop FALSE, subsetting possible components CompositionMatrix object produce closed subcomposition (see examples).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object — subset","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of an Object — subset","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048  ## Subset coda[[1, 1]] # Get the first value #> [1] 0.488 coda[1] # Get the first value #> [1] 0.488 coda[, ] # Get all values #> <CompositionMatrix: 25 x 5> #>             A         B          C          D          E #> H1  0.4880000 0.3170000 0.03800000 0.06400000 0.09300000 #> H2  0.4820000 0.2380000 0.09000000 0.09200000 0.09800000 #> H3  0.3700000 0.0910000 0.34200000 0.09500000 0.10200000 #> H4  0.5090000 0.2380000 0.07200000 0.10100000 0.08000000 #> H5  0.4420000 0.3830000 0.02900000 0.07700000 0.06900000 #> H6  0.5230000 0.2620000 0.04200000 0.12500000 0.04800000 #> H7  0.4460000 0.3300000 0.04600000 0.12200000 0.05600000 #> H8  0.3460000 0.0520000 0.42900000 0.09600000 0.07700000 #> H9  0.4120000 0.1170000 0.26700000 0.09600000 0.10800000 #> H10 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000 #> H11 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000 #> H12 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000 #> H13 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000 #> H14 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000 #> H15 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000 #> H16 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000 #> H17 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000 #> H18 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306 #> H19 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000 #> H20 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000 #> H21 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000 #> H22 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000 #> H23 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000 #> H24 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000 #> H25 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000 coda[1, , drop = FALSE] # Get the first row #> <CompositionMatrix: 1 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093  ## Subcomposition subcoda <- coda[, 1:3, drop = FALSE] # Get the first three column head(subcoda) #> <CompositionMatrix: 6 x 3> #>            A         B          C #> H1 0.5788849 0.3760380 0.04507711 #> H2 0.5950617 0.2938272 0.11111111 #> H3 0.4607721 0.1133250 0.42590286 #> H4 0.6214896 0.2905983 0.08791209 #> H5 0.5175644 0.4484778 0.03395785 #> H6 0.6324063 0.3168077 0.05078597"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":null,"dir":"Reference","previous_headings":"","what":"Row Sums — totals","title":"Row Sums — totals","text":"Retrieves defines row sums (closure).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row Sums — totals","text":"","code":"get_totals(x)  set_totals(x) <- value  # S4 method for CompositionMatrix get_totals(x)  # S4 method for CompositionMatrix set_totals(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row Sums — totals","text":"x object get set totals. value possible value totals x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row Sums — totals","text":"set_totals() returns object sort x new row sums assigned. get_totals() returns row sums x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Row Sums — totals","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row Sums — totals","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 120 182 215 197 209 257 262 182 283 378 290 232 165 221 273 406 307 272 337 #> [20] 233  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>            V1         V2         V3         V4         V5 samples groups #> S1 0.53333333 0.30833333 0.03333333 0.06666667 0.05833333      S1   <NA> #> S2 0.15934066 0.49450549 0.15384615 0.06593407 0.12637363      S2   <NA> #> S3 0.02325581 0.05581395 0.37209302 0.31162791 0.23720930      S3   <NA> #> S4 0.03553299 0.32487310 0.29441624 0.22842640 0.11675127      S4   <NA> #> S5 0.29665072 0.20574163 0.22966507 0.22009569 0.04784689      S5   <NA> #> S6 0.08560311 0.31906615 0.03501946 0.38521401 0.17509728      S6   <NA>"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive Log-Ratios (ALR) — transform_alr","title":"Additive Log-Ratios (ALR) — transform_alr","text":"Computes ALR transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive Log-Ratios (ALR) — transform_alr","text":"","code":"transform_alr(object, ...)  # S4 method for CompositionMatrix transform_alr(object, j = ncol(object))"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive Log-Ratios (ALR) — transform_alr","text":"object CompositionMatrix object. ... Currently used. j integer giving index rationing part (denominator).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive Log-Ratios (ALR) — transform_alr","text":"ALR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Additive Log-Ratios (ALR) — transform_alr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additive Log-Ratios (ALR) — transform_alr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additive Log-Ratios (ALR) — transform_alr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered Log-Ratios (CLR) — transform_clr","title":"Centered Log-Ratios (CLR) — transform_clr","text":"Computes CLR transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered Log-Ratios (CLR) — transform_clr","text":"","code":"transform_clr(object, ...)  # S4 method for CompositionMatrix transform_clr(object, weights = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Centered Log-Ratios (CLR) — transform_clr","text":"object CompositionMatrix object. ... Currently used. weights logical scalar: sould varying weight used. FALSE (default), equally-weighted parts used. Alternatively, positive numeric vector weights can specified.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Centered Log-Ratios (CLR) — transform_clr","text":"CLR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Centered Log-Ratios (CLR) — transform_clr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Centered Log-Ratios (CLR) — transform_clr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Centered Log-Ratios (CLR) — transform_clr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Isometric Log-Ratios (ILR) — transform_ilr","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"Computes ILR transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"","code":"transform_ilr(object, ...)  # S4 method for CompositionMatrix transform_ilr(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"ILR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"ILR transformation provides coordinates composition respect given orthonormal basis. transform_ilr() uses orthonormal basis (Helmert matrix) originally defined Egozcue et al. (2003).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Log-Ratio Transformation — transform_inverse","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"Computes inverse log-ratio transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"","code":"transform_inverse(object, ...)  # S4 method for CLR transform_inverse(object)  # S4 method for ALR transform_inverse(object)  # S4 method for ILR transform_inverse(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Fišerová, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455‑468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise Log-Ratios (LR) — transform_lr","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"Computes pairwise log-ratio transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"","code":"transform_lr(object, ...)  # S4 method for CompositionMatrix transform_lr(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"LR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot Log-Ratios (PLR) — transform_plr","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"Computes PLR transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"","code":"transform_plr(object, ...)  # S4 method for CompositionMatrix transform_plr(object, pivot = 1)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"object CompositionMatrix object. ... Currently used. pivot integer giving index pivotal variable.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"PLR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"Fišerová, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455‑468. doi:10.1007/s11004-011-9333-x . Hron, K., Filzmoser, P., de Caritat, P., Fišerová, E. & Gardlo, . (2017). Weighted Pivot Coordinates Compositional Data Application Geochemical Mapping. Mathematical Geosciences, 49(6), 797-814. doi:10.1007/s11004-017-9684-z . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":null,"dir":"Reference","previous_headings":"","what":"Variation Matrix — variation","title":"Variation Matrix — variation","text":"Computes variation matrix (Aitchison 1986, definition 4.4).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variation Matrix — variation","text":"","code":"variation(x, ...)  # S4 method for CompositionMatrix variation(x)"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variation Matrix — variation","text":"x CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variation Matrix — variation","text":"matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variation Matrix — variation","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variation Matrix — variation","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variation Matrix — variation","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Variation matrix ## (Aitchison 1986, definition 4.4) (varia <- variation(coda)) #>            A         B         C          D         E #> A 0.00000000 0.2592742 1.5328586 0.08281464 0.1385604 #> B 0.25927416 0.0000000 3.0006804 0.54727146 0.6490135 #> C 1.53285862 3.0006804 0.0000000 1.11145146 0.9476375 #> D 0.08281464 0.5472715 1.1114515 0.00000000 0.1870579 #> E 0.13856035 0.6490135 0.9476375 0.18705791 0.0000000  ## Cluster dendrogram d <- as.dist(varia) h <- hclust(d, method = \"ward.D2\") plot(h)"}]