<!-- Generated by pkgdown: do not edit by hand --><htmllang="en"><head><metahttp-equiv="Content-Type"content="text/html; charset=UTF-8"><metacharset="utf-8"><metahttp-equiv="X-UA-Compatible"content="IE=edge"><metaname="viewport"content="width=device-width, initial-scale=1.0"><title>Principal Component Analysis (for AMR) — pca • AMR (for R)</title><!-- favicons --><linkrel="icon"type="image/png"sizes="16x16"href="../favicon-16x16.png"><linkrel="icon"type="image/png"sizes="32x32"href="../favicon-32x32.png"><linkrel="apple-touch-icon"type="image/png"sizes="180x180"href="../apple-touch-icon.png"><linkrel="apple-touch-icon"type="image/png"sizes="120x120"href="../apple-touch-icon-120x120.png"><linkrel="apple-touch-icon"type="image/png"sizes="76x76"href="../apple-touch-icon-76x76.png"><linkrel="apple-touch-icon"type="image/png"sizes="60x60"href="../apple-touch-icon-60x60.png"><!-- jquery --><scriptsrc="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js"integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo="crossorigin="anonymous"></script><!-- Bootstrap --><linkhref="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/flatly/bootstrap.min.css"rel="stylesheet"crossorigin="anonymous"><scriptsrc="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js"integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4="crossorigin="anonymous"></script><!-- bootstrap-toc --><linkrel="stylesheet"href="../bootstrap-toc.css"><scriptsrc="../bootstrap-toc.js"></script><!-- Font Awesome icons --><linkrel="stylesheet"href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css"integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk="crossorigin="anonymous"><linkrel="stylesheet"href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css"integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw="crossorigin="anonymous"><!-- clipboard.js --><scriptsrc="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js"integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI="crossorigin="anonymous"></script><!-- headroom.js --><scriptsrc="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js"integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0="crossorigin="anonymous"></script><scriptsrc="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js"integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4="crossorigin="anonymous"></script><!-- pkgdown --><linkhref="../pkgdown.css"rel="stylesheet"><scriptsrc="../pkgdown.js"></script><linkhref="../extra.css"rel="stylesheet"><scriptsrc="../extra.js"></script><metaproperty="og:title"content="Principal Component Analysis (for AMR) — pca"><metaproperty="og:description"content="Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables."><metaproperty="og:image"content="https://msberends.github.io/AMR/logo.svg"><metaname="twitter:card"content="summary_large_image"><metaname="twitter:creator"content="@msberends"><metaname="twitter:site"content="@univgroningen"><!-- mathjax --><scriptsrc="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k="crossorigin="anonymous"></script><scriptsrc="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js"integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA="crossorigin="anonymous"></script><!--[if lt IE 9]>
<p>Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.</p>
desired rank is considerably smaller than the dimensions of the matrix.</p></dd>
</dl></div>
<divid="value">
<h2>Value</h2>
<p>An object of classes pca and <ahref="https://rdrr.io/r/stats/prcomp.html"class="external-link">prcomp</a></p>
</div>
<divid="details">
<h2>Details</h2>
<p>The <code>pca()</code> function takes a <ahref="https://rdrr.io/r/base/data.frame.html"class="external-link">data.frame</a> as input and performs the actual PCA with the <spanstyle="R">R</span> function <code><ahref="https://rdrr.io/r/stats/prcomp.html"class="external-link">prcomp()</a></code>.</p>
<p>The result of the <code>pca()</code> function is a <ahref="https://rdrr.io/r/stats/prcomp.html"class="external-link">prcomp</a> object, with an additional attribute <code>non_numeric_cols</code> which is a vector with the column names of all columns that do not contain <ahref="https://rdrr.io/r/base/numeric.html"class="external-link">numeric</a> values. These are probably the groups and labels, and will be used by <code><ahref="ggplot_pca.html">ggplot_pca()</a></code>.</p>
The <ahref="lifecycle.html">lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
<p>If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
<p>On our website <ahref="https://msberends.github.io/AMR/">https://msberends.github.io/AMR/</a> you can find <ahref="https://msberends.github.io/AMR/articles/AMR.html">a comprehensive tutorial</a> about how to conduct AMR data analysis, the <ahref="https://msberends.github.io/AMR/reference/">complete documentation of all functions</a> and <ahref="https://msberends.github.io/AMR/articles/WHONET.html">an example analysis using WHONET data</a>.</p>
<spanclass="fu"><ahref="https://dplyr.tidyverse.org/reference/group_by.html"class="external-link">group_by</a></span><spanclass="op">(</span>order <spanclass="op">=</span><spanclass="fu"><ahref="mo_property.html">mo_order</a></span><spanclass="op">(</span><spanclass="va">mo</span><spanclass="op">)</span>, <spanclass="co"># group on anything, like order</span>
genus <spanclass="op">=</span><spanclass="fu"><ahref="mo_property.html">mo_genus</a></span><spanclass="op">(</span><spanclass="va">mo</span><spanclass="op">)</span><spanclass="op">)</span><spanclass="op"><ahref="https://magrittr.tidyverse.org/reference/pipe.html"class="external-link">%>%</a></span><spanclass="co"># and genus as we do here;</span>
<spanclass="fu"><ahref="https://dplyr.tidyverse.org/reference/summarise_all.html"class="external-link">summarise_if</a></span><spanclass="op">(</span><spanclass="va">is.rsi</span>, <spanclass="va">resistance</span><spanclass="op">)</span><spanclass="co"># then get resistance of all drugs</span>
<spanclass="fu"><ahref="ggplot_pca.html">ggplot_pca</a></span><spanclass="op">(</span><spanclass="va">pca_result</span><spanclass="op">)</span><spanclass="co"># a new and convenient plot function</span>
