<!-- 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.png"><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 an 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>
