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<h1>Principal Component Analysis (for AMR)</h1>
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<small class="dont-index">Source: <a href='https://gitlab.com/msberends/AMR/blob/master/R/pca.R'><code>R/pca.R</code></a></small>
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<div class="hidden name"><code>pca.Rd</code></div>
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</div>
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<div class="ref-description">
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<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>
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</div>
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<pre class="usage"><span class='fu'>pca</span>(
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<span class='no'>x</span>,
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<span class='no'>...</span>,
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<span class='kw'>retx</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
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<span class='kw'>center</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
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<span class='kw'>scale.</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
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<span class='kw'>tol</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
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<span class='kw'>rank.</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
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)</pre>
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<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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<table class="ref-arguments">
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<colgroup><col class="name" /><col class="desc" /></colgroup>
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<tr>
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<th>x</th>
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<td><p>a <a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> containing numeric columns</p></td>
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</tr>
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<tr>
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<th>...</th>
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<td><p>columns of <code>x</code> to be selected for PCA, can be unquoted since it supports quasiquotation.</p></td>
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</tr>
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<tr>
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<th>retx</th>
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<td><p>a logical value indicating whether the rotated variables
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should be returned.</p></td>
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</tr>
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<tr>
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<th>center</th>
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<td><p>a logical value indicating whether the variables
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should be shifted to be zero centered. Alternately, a vector of
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length equal the number of columns of <code>x</code> can be supplied.
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The value is passed to <code>scale</code>.</p></td>
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</tr>
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<tr>
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<th>scale.</th>
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<td><p>a logical value indicating whether the variables should
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be scaled to have unit variance before the analysis takes
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place. The default is <code>FALSE</code> for consistency with S, but
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in general scaling is advisable. Alternatively, a vector of length
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equal the number of columns of <code>x</code> can be supplied. The
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value is passed to <code><a href='https://rdrr.io/r/base/scale.html'>scale</a></code>.</p></td>
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</tr>
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<tr>
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<th>tol</th>
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<td><p>a value indicating the magnitude below which components
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should be omitted. (Components are omitted if their
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standard deviations are less than or equal to <code>tol</code> times the
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standard deviation of the first component.) With the default null
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setting, no components are omitted (unless <code>rank.</code> is specified
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less than <code><a href='https://rdrr.io/r/base/Extremes.html'>min(dim(x))</a></code>.). Other settings for tol could be
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<code>tol = 0</code> or <code>tol = sqrt(.Machine$double.eps)</code>, which
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would omit essentially constant components.</p></td>
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</tr>
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<tr>
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<th>rank.</th>
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<td><p>optionally, a number specifying the maximal rank, i.e.,
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maximal number of principal components to be used. Can be set as
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alternative or in addition to <code>tol</code>, useful notably when the
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desired rank is considerably smaller than the dimensions of the matrix.</p></td>
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</tr>
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</table>
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<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
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<p>An object of classes pca and <a href='https://rdrr.io/r/stats/prcomp.html'>prcomp</a></p>
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<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
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<p>The <code>pca()</code> function takes a <a href='https://rdrr.io/r/base/data.frame.html'>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'>prcomp()</a></code>.</p>
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<p>The result of the <code>pca()</code> function is a <a href='https://rdrr.io/r/stats/prcomp.html'>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 numeric values. These are probably the groups and labels, and will be used by <code><a href='ggplot_pca.html'>ggplot_pca()</a></code>.</p>
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<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing lifecycle</h2>
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<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
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The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. This function needs wider usage and more extensive testing in order to optimise the unlying code.</p>
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<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
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<pre class="examples"><span class='co'># `example_isolates` is a dataset available in the AMR package.</span>
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<span class='co'># See ?example_isolates.</span>
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<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
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<span class='co'># calculate the resistance per group first</span>
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<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>dplyr</span>)
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<span class='no'>resistance_data</span> <span class='kw'><-</span> <span class='no'>example_isolates</span> <span class='kw'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span>(<span class='kw'>order</span> <span class='kw'>=</span> <span class='fu'><a href='mo_property.html'>mo_order</a></span>(<span class='no'>mo</span>), <span class='co'># group on anything, like order</span>
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<span class='kw'>genus</span> <span class='kw'>=</span> <span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='no'>mo</span>)) <span class='kw'>%>%</span> <span class='co'># and genus as we do here</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/summarise_all.html'>summarise_if</a></span>(<span class='no'>is.rsi</span>, <span class='no'>resistance</span>) <span class='co'># then get resistance of all drugs</span>
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<span class='co'># now conduct PCA for certain antimicrobial agents</span>
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<span class='no'>pca_result</span> <span class='kw'><-</span> <span class='no'>resistance_data</span> <span class='kw'>%>%</span>
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<span class='fu'>pca</span>(<span class='no'>AMC</span>, <span class='no'>CXM</span>, <span class='no'>CTX</span>, <span class='no'>CAZ</span>, <span class='no'>GEN</span>, <span class='no'>TOB</span>, <span class='no'>TMP</span>, <span class='no'>SXT</span>)
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<span class='no'>pca_result</span>
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<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>pca_result</span>)
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<span class='fu'><a href='https://rdrr.io/r/stats/biplot.html'>biplot</a></span>(<span class='no'>pca_result</span>)
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<span class='fu'><a href='ggplot_pca.html'>ggplot_pca</a></span>(<span class='no'>pca_result</span>) <span class='co'># a new and convenient plot function</span>
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}</pre>
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<p>Developed by <a href='https://www.rug.nl/staff/m.s.berends/'>Matthijs S. Berends</a>, <a href='https://www.rug.nl/staff/c.f.luz/'>Christian F. Luz</a>, <a href='https://www.rug.nl/staff/a.w.friedrich/'>Alexander W. Friedrich</a>, <a href='https://www.rug.nl/staff/b.sinha/'>Bhanu N. M. Sinha</a>, <a href='https://www.rug.nl/staff/c.j.albers/'>Casper J. Albers</a>, <a href='https://www.rug.nl/staff/c.glasner/'>Corinna Glasner</a>.</p>
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