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<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="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."><title>Principal Component Analysis (for AMR) — pca • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="../deps/Fira_Code-0.4.6/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Principal Component Analysis (for AMR) — pca"><meta property="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."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><meta name="twitter:card" content="summary_large_image"><meta name="twitter:creator" content="@msberends"><meta name="twitter:site" content="@msberends"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="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]>
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<img src="../logo.svg" class="logo" alt=""><h1>Principal Component Analysis (for AMR)</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/R/pca.R" class="external-link"><code>R/pca.R</code></a></small>
<div class="d-none name"><code>pca.Rd</code></div>
</div>
<div class="ref-description section level2">
<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>
</div>
<div class="section level2">
<h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">pca</span><span class="op">(</span></span>
<span> <span class="va">x</span>,</span>
<span> <span class="va">...</span>,</span>
<span> retx <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> center <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> scale. <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> tol <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> rank. <span class="op">=</span> <span class="cn">NULL</span></span>
<span><span class="op">)</span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
<dl><dt>x</dt>
<dd><p>a <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a> containing <a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a> columns</p></dd>
<dt>...</dt>
<dd><p>columns of <code>x</code> to be selected for PCA, can be unquoted since it supports quasiquotation.</p></dd>
<dt>retx</dt>
<dd><p>a logical value indicating whether the rotated variables
should be returned.</p></dd>
<dt>center</dt>
<dd><p>a logical value indicating whether the variables
should be shifted to be zero centered. Alternately, a vector of
length equal the number of columns of <code>x</code> can be supplied.
The value is passed to <code>scale</code>.</p></dd>
<dt>scale.</dt>
<dd><p>a logical value indicating whether the variables should
be scaled to have unit variance before the analysis takes
place. The default is <code>FALSE</code> for consistency with S, but
in general scaling is advisable. Alternatively, a vector of length
equal the number of columns of <code>x</code> can be supplied. The
value is passed to <code><a href="https://rdrr.io/r/base/scale.html" class="external-link">scale</a></code>.</p></dd>
<dt>tol</dt>
<dd><p>a value indicating the magnitude below which components
should be omitted. (Components are omitted if their
standard deviations are less than or equal to <code>tol</code> times the
standard deviation of the first component.) With the default null
setting, no components are omitted (unless <code>rank.</code> is specified
less than <code>min(dim(x))</code>.). Other settings for tol could be
<code>tol = 0</code> or <code>tol = sqrt(.Machine$double.eps)</code>, which
would omit essentially constant components.</p></dd>
<dt>rank.</dt>
<dd><p>optionally, a number specifying the maximal rank, i.e.,
maximal number of principal components to be used. Can be set as
alternative or in addition to <code>tol</code>, useful notably when the
desired rank is considerably smaller than the dimensions of the matrix.</p></dd>
</dl></div>
<div class="section level2">
<h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
<p>An object of classes pca and <a href="https://rdrr.io/r/stats/prcomp.html" class="external-link">prcomp</a></p>
</div>
<div class="section level2">
<h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
<p>The <code>pca()</code> function takes a <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a> as input and performs the actual PCA with the <span style="R">R</span> function <code><a href="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 <a href="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 <a href="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><a href="ggplot_pca.html">ggplot_pca()</a></code>.</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"># `example_isolates` is a data set available in the AMR package.</span></span></span>
<span class="r-in"><span><span class="co"># See ?example_isolates.</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># \donttest{</span></span></span>
<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span> <span class="co"># calculate the resistance per group first</span></span></span>
<span class="r-in"><span> <span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span></span>
<span class="r-in"><span> order <span class="op">=</span> <span class="fu"><a href="mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span></span></span>
<span class="r-in"><span> genus <span class="op">=</span> <span class="fu"><a href="mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># and genus as we do here;</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span> <span class="op">&gt;=</span> <span class="fl">30</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># filter on only 30 results per group</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.sir</span>, <span class="va">resistance</span><span class="op">)</span> <span class="co"># then get resistance of all drugs</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="co"># now conduct PCA for certain antimicrobial drugs</span></span></span>
<span class="r-in"><span> <span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="va">resistance_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span> <span class="fu">pca</span><span class="op">(</span><span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>, <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="va">pca_result</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="co"># old base R plotting method:</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="co"># new ggplot2 plotting method using this package:</span></span></span>
<span class="r-in"><span> <span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://ggplot2.tidyverse.org" class="external-link">"ggplot2"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="fu"><a href="ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_colour_viridis_d</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Title here"</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="op">}</span></span></span>
<span class="r-in"><span><span class="op">}</span></span></span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>There were 73 warnings in `summarise()`.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> The first warning was:</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE,</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> only_all_tested = FALSE) ...`.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> Caused by warning:</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> Introducing NA: only 14 results available for PEN in group: order =</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> "Lactobacillales", genus = "Enterococcus" (minimum = 30).</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings.</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT",</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> "TMP", and "TOB". Total observations available: 7.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Groups (n=4, named as 'order'):</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales" </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-plt img"><img src="pca-1.png" alt="" width="700" height="433"></span>
<span class="r-plt img"><img src="pca-2.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
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