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(v1.0.1.9000) first PCA implementation

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dr. M.S. (Matthijs) Berends
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1.9000</span>
</span>
</div>
@ -115,6 +115,13 @@
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress-alt"></span>
Conduct Principal Component Analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
@ -250,7 +257,7 @@
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>An integer (no decimals) if <code>exact = FALSE</code>, a double (with decimals) otherwise</p>
<p>An <a href='https://rdrr.io/r/base/integer.html'>integer</a> (no decimals) if <code>exact = FALSE</code>, a <a href='https://rdrr.io/r/base/double.html'>double</a> (with decimals) otherwise</p>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable lifecycle</h2>

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1.9000</span>
</span>
</div>
@ -115,6 +115,13 @@
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress-alt"></span>
Conduct Principal Component Analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
@ -240,23 +247,23 @@
</tr>
<tr>
<th>na.rm</th>
<td><p>a logical to indicate whether missing values should be removed</p></td>
<td><p>a <a href='https://rdrr.io/r/base/logical.html'>logical</a> to indicate whether missing values should be removed</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>Ordered <code><a href='https://rdrr.io/r/base/factor.html'>factor</a></code></p>
<p>Ordered <a href='https://rdrr.io/r/base/factor.html'>factor</a></p>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>To split ages, the input can be:</p><ul>
<p>To split ages, the input for the <code>split_at</code> parameter can be:</p><ul>
<li><p>A numeric vector. A vector of e.g. <code><a href='https://rdrr.io/r/base/c.html'>c(10, 20)</a></code> will split on 0-9, 10-19 and 20+. A value of only <code>50</code> will split on 0-49 and 50+.
The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).</p></li>
<li><p>A character:</p><ul>
<li><p><code>"children"</code> or <code>"kids"</code>, equivalent of: <code><a href='https://rdrr.io/r/base/c.html'>c(0, 1, 2, 4, 6, 13, 18)</a></code>. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.</p></li>
<li><p><code>"elderly"</code> or <code>"seniors"</code>, equivalent of: <code><a href='https://rdrr.io/r/base/c.html'>c(65, 75, 85)</a></code>. This will split on 0-64, 65-74, 75-84, 85+.</p></li>
<li><p><code>"fives"</code>, equivalent of: <code>1:20 * 5</code>. This will split on 0-4, 5-9, 10-14, ..., 90-94, 95-99, 100+.</p></li>
<li><p><code>"tens"</code>, equivalent of: <code>1:10 * 10</code>. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.</p></li>
<li><p><code>"tens"</code>, equivalent of: <code>1:10 * 10</code>. This will split on 0-9, 10-19, 20-29, ..., 80-89, 90-99, 100+.</p></li>
</ul></li>
</ul>

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<h1>PCA biplot with <code>ggplot2</code></h1>
<div class="hidden name"><code>ggplot_pca.Rd</code></div>
</div>
<div class="ref-description">
<p>This function is to produce a <code>ggplot2</code> variant of a so-called <a href='https://en.wikipedia.org/wiki/Biplot'>biplot</a> for PCA (principal component analysis), but is more flexible and more appealing than the base <span style="R">R</span> <code><a href='https://rdrr.io/r/stats/biplot.html'>biplot()</a></code> function.</p>
</div>
<pre class="usage"><span class='fu'>ggplot_pca</span>(
<span class='no'>x</span>,
<span class='kw'>choices</span> <span class='kw'>=</span> <span class='fl'>1</span>:<span class='fl'>2</span>,
<span class='kw'>scale</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>labels</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>labels_textsize</span> <span class='kw'>=</span> <span class='fl'>3</span>,
<span class='kw'>labels_text_placement</span> <span class='kw'>=</span> <span class='fl'>1.5</span>,
<span class='kw'>groups</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>ellipse</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
<span class='kw'>ellipse_prob</span> <span class='kw'>=</span> <span class='fl'>0.68</span>,
<span class='kw'>ellipse_size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>,
<span class='kw'>ellipse_alpha</span> <span class='kw'>=</span> <span class='fl'>0.25</span>,
<span class='kw'>points_size</span> <span class='kw'>=</span> <span class='fl'>2</span>,
<span class='kw'>points_alpha</span> <span class='kw'>=</span> <span class='fl'>0.25</span>,
<span class='kw'>arrows</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>arrows_colour</span> <span class='kw'>=</span> <span class='st'>"darkblue"</span>,
<span class='kw'>arrows_size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>,
<span class='kw'>arrows_textsize</span> <span class='kw'>=</span> <span class='fl'>3</span>,
<span class='kw'>arrows_alpha</span> <span class='kw'>=</span> <span class='fl'>0.75</span>,
<span class='kw'>base_textsize</span> <span class='kw'>=</span> <span class='fl'>10</span>,
<span class='no'>...</span>
)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>x</th>
<td><p>an object returned by <code><a href='pca.html'>pca()</a></code>, <code><a href='https://rdrr.io/r/stats/prcomp.html'>prcomp()</a></code> or <code><a href='https://rdrr.io/r/stats/princomp.html'>princomp()</a></code></p></td>
</tr>
<tr>
<th>choices</th>
<td><p>length 2 vector specifying the components to plot. Only the default
is a biplot in the strict sense.</p></td>
</tr>
<tr>
<th>scale</th>
<td><p>The variables are scaled by <code>lambda ^ scale</code> and the
observations are scaled by <code>lambda ^ (1-scale)</code> where
<code>lambda</code> are the singular values as computed by
<code><a href='https://rdrr.io/r/stats/princomp.html'>princomp</a></code>. Normally <code>0 &lt;= scale &lt;= 1</code>, and a warning
will be issued if the specified <code>scale</code> is outside this range.</p></td>
</tr>
<tr>
<th>labels</th>
<td><p>an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the <code><a href='pca.html'>pca()</a></code> function as input for <code>x</code>, this will be determined automatically based on the attribute <code>non_numeric_cols</code>, see <code><a href='pca.html'>pca()</a></code>.</p></td>
</tr>
<tr>
<th>labels_textsize</th>
<td><p>the size of the text used for the labels</p></td>
</tr>
<tr>
<th>labels_text_placement</th>
<td><p>adjustment factor the placement of the variable names (<code>&gt;=1</code> means further away from the arrow head)</p></td>
</tr>
<tr>
<th>groups</th>
<td><p>an optional vector of groups for the labels, with the same length as <code>labels</code>. If set, the points and labels will be coloured according to these groups. When using the <code><a href='pca.html'>pca()</a></code> function as input for <code>x</code>, this will be determined automatically based on the attribute <code>non_numeric_cols</code>, see <code><a href='pca.html'>pca()</a></code>.</p></td>
</tr>
<tr>
<th>ellipse</th>
<td><p>a logical to indicate whether a normal data ellipse should be drawn for each group (set with <code>groups</code>)</p></td>
</tr>
<tr>
<th>ellipse_prob</th>
<td><p>statistical size of the ellipse in normal probability</p></td>
</tr>
<tr>
<th>ellipse_size</th>
<td><p>the size of the ellipse line</p></td>
</tr>
<tr>
<th>ellipse_alpha</th>
<td><p>the alpha (transparency) of the ellipse line</p></td>
</tr>
<tr>
<th>points_alpha</th>
<td><p>the alpha (transparency) of the points</p></td>
</tr>
<tr>
<th>arrows</th>
<td><p>a logical to indicate whether arrows should be drawn</p></td>
</tr>
<tr>
<th>arrows_colour</th>
<td><p>the colour of the arrow and their text</p></td>
</tr>
<tr>
<th>arrows_size</th>
<td><p>the size (thickness) of the arrow lines</p></td>
</tr>
<tr>
<th>arrows_textsize</th>
<td><p>the size of the text at the end of the arrows</p></td>
</tr>
<tr>
<th>arrows_alpha</th>
<td><p>the alpha (transparency) of the arrows and their text</p></td>
</tr>
<tr>
<th>base_textsize</th>
<td><p>the text size for all plot elements except the labels and arrows</p></td>
</tr>
<tr>
<th>...</th>
<td><p>Parameters passed on to functions</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
<p>The <code>ggplot_pca()</code> function is based on the <code>ggbiplot()</code> function from the <code>ggbiplot</code> package by Vince Vu, as found on GitHub: <a href='https://github.com/vqv/ggbiplot'>https://github.com/vqv/ggbiplot</a> (retrieved: 2 March 2020, their latest commit: <a href='https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9'><code>7325e88</code></a>; 12 February 2015).</p>
<p>As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were:</p><ol>
<li><p>Rewritten code to remove the dependency on packages <code>plyr</code>, <code>scales</code> and <code>grid</code></p></li>
<li><p>Parametrised more options, like arrow and ellipse settings</p></li>
<li><p>Added total amount of explained variance as a caption in the plot</p></li>
<li><p>Cleaned all syntax based on the <code>lintr</code> package</p></li>
<li><p>Updated documentation</p></li>
</ol>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>The default colours for labels and points is set with <code>scale_colour_viridis_d()</code>, but these can be changed by adding another scale for colour, like <code>scale_colour_brewer()</code>.</p>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing lifecycle</h2>
<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
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. We will strive to maintain backward compatibility, but the function needs wider usage and more extensive testing in order to optimise the unlying code.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'># `example_isolates` is a dataset available in the AMR package.</span>
<span class='co'># See ?example_isolates.</span>
<span class='co'># See ?pca for more info about Principal Component Analysis (PCA).</span>
<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>dplyr</span>)
<span class='no'>pca_model</span> <span class='kw'>&lt;-</span> <span class='no'>example_isolates</span> <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='no'>mo</span>) <span class='kw'>==</span> <span class='st'>"Staphylococcus"</span>) <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span>(<span class='kw'>species</span> <span class='kw'>=</span> <span class='fu'><a href='mo_property.html'>mo_shortname</a></span>(<span class='no'>mo</span>)) <span class='kw'>%&gt;%</span>
<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='kw'>%&gt;%</span>
<span class='fu'><a href='pca.html'>pca</a></span>(<span class='no'>FLC</span>, <span class='no'>AMC</span>, <span class='no'>CXM</span>, <span class='no'>GEN</span>, <span class='no'>TOB</span>, <span class='no'>TMP</span>, <span class='no'>SXT</span>, <span class='no'>CIP</span>, <span class='no'>TEC</span>, <span class='no'>TCY</span>, <span class='no'>ERY</span>)
<span class='co'># old</span>
<span class='fu'><a href='https://rdrr.io/r/stats/biplot.html'>biplot</a></span>(<span class='no'>pca_model</span>)
<span class='co'># new </span>
<span class='fu'>ggplot_pca</span>(<span class='no'>pca_model</span>)</pre>
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<li><a href="#examples">Examples</a></li>
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73
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@ -78,7 +78,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1.9000</span>
</span>
</div>
@ -114,6 +114,13 @@
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
@ -287,12 +294,6 @@
<p><code><a href="read.4D.html">read.4D()</a></code> </p>
</td>
<td><p>Read data from 4D database</p></td>
</tr><tr>
<td>
<p><code><a href="rsi_translation.html">rsi_translation</a></code> </p>
</td>
<td><p>Data set for R/SI interpretation</p></td>
</tr>
</tbody><tbody>
<tr>
@ -371,6 +372,18 @@
</tr>
<tr>
<td>
<p><code><a href="proportion.html">resistance()</a></code> <code><a href="proportion.html">susceptibility()</a></code> <code><a href="proportion.html">proportion_R()</a></code> <code><a href="proportion.html">proportion_IR()</a></code> <code><a href="proportion.html">proportion_I()</a></code> <code><a href="proportion.html">proportion_SI()</a></code> <code><a href="proportion.html">proportion_S()</a></code> <code><a href="proportion.html">proportion_df()</a></code> <code><a href="proportion.html">rsi_df()</a></code> </p>
</td>
<td><p>Calculate microbial resistance</p></td>
</tr><tr>
<td>
<p><code><a href="count.html">count_resistant()</a></code> <code><a href="count.html">count_susceptible()</a></code> <code><a href="count.html">count_R()</a></code> <code><a href="count.html">count_IR()</a></code> <code><a href="count.html">count_I()</a></code> <code><a href="count.html">count_SI()</a></code> <code><a href="count.html">count_S()</a></code> <code><a href="count.html">count_all()</a></code> <code><a href="count.html">n_rsi()</a></code> <code><a href="count.html">count_df()</a></code> </p>
</td>
<td><p>Count available isolates</p></td>
</tr><tr>
<td>
<p><code><a href="availability.html">availability()</a></code> </p>
</td>
@ -384,9 +397,15 @@
</tr><tr>
<td>
<p><code><a href="count.html">count_resistant()</a></code> <code><a href="count.html">count_susceptible()</a></code> <code><a href="count.html">count_R()</a></code> <code><a href="count.html">count_IR()</a></code> <code><a href="count.html">count_I()</a></code> <code><a href="count.html">count_SI()</a></code> <code><a href="count.html">count_S()</a></code> <code><a href="count.html">count_all()</a></code> <code><a href="count.html">n_rsi()</a></code> <code><a href="count.html">count_df()</a></code> </p>
<p><code><a href="resistance_predict.html">resistance_predict()</a></code> <code><a href="resistance_predict.html">rsi_predict()</a></code> <code><a href="resistance_predict.html">plot(<i>&lt;resistance_predict&gt;</i>)</a></code> <code><a href="resistance_predict.html">ggplot_rsi_predict()</a></code> </p>
</td>
<td><p>Count available isolates</p></td>
<td><p>Predict antimicrobial resistance</p></td>
</tr><tr>
<td>
<p><code><a href="pca.html">prcomp(<i>&lt;data.frame&gt;</i>)</a></code> <code><a href="pca.html">pca()</a></code> </p>
</td>
<td><p>Principal Component Analysis (for AMR)</p></td>
</tr><tr>
<td>
@ -407,24 +426,18 @@
<td><p>AMR plots with <code>ggplot2</code></p></td>
</tr><tr>
<td>
<p><code><a href="ggplot_pca.html">ggplot_pca()</a></code> </p>
</td>
<td><p>PCA biplot with <code>ggplot2</code></p></td>
</tr><tr>
<td>
<p><code><a href="kurtosis.html">kurtosis()</a></code> </p>
</td>
<td><p>Kurtosis of the sample</p></td>
</tr><tr>
<td>
<p><code><a href="proportion.html">resistance()</a></code> <code><a href="proportion.html">susceptibility()</a></code> <code><a href="proportion.html">proportion_R()</a></code> <code><a href="proportion.html">proportion_IR()</a></code> <code><a href="proportion.html">proportion_I()</a></code> <code><a href="proportion.html">proportion_SI()</a></code> <code><a href="proportion.html">proportion_S()</a></code> <code><a href="proportion.html">proportion_df()</a></code> <code><a href="proportion.html">rsi_df()</a></code> </p>
</td>
<td><p>Calculate microbial resistance</p></td>
</tr><tr>
<td>
<p><code><a href="resistance_predict.html">resistance_predict()</a></code> <code><a href="resistance_predict.html">rsi_predict()</a></code> <code><a href="resistance_predict.html">plot(<i>&lt;resistance_predict&gt;</i>)</a></code> <code><a href="resistance_predict.html">ggplot_rsi_predict()</a></code> </p>
</td>
<td><p>Predict antimicrobial resistance</p></td>
</tr><tr>
<td>
<p><code><a href="skewness.html">skewness()</a></code> </p>
</td>
@ -439,6 +452,12 @@
</tr>
<tr>
<td>
<p><code><a href="microorganisms.html">microorganisms</a></code> </p>
</td>
<td><p>Data set with ~70,000 microorganisms</p></td>
</tr><tr>
<td>
<p><code><a href="antibiotics.html">antibiotics</a></code> <code><a href="antibiotics.html">antivirals</a></code> </p>
</td>
@ -457,6 +476,12 @@
<td><p>Data set with unclean data</p></td>
</tr><tr>
<td>
<p><code><a href="rsi_translation.html">rsi_translation</a></code> </p>
</td>
<td><p>Data set for R/SI interpretation</p></td>
</tr><tr>
<td>
<p><code><a href="microorganisms.codes.html">microorganisms.codes</a></code> </p>
</td>
@ -469,12 +494,6 @@
<td><p>Data set with previously accepted taxonomic names</p></td>
</tr><tr>
<td>
<p><code><a href="microorganisms.html">microorganisms</a></code> </p>
</td>
<td><p>Data set with ~70,000 microorganisms</p></td>
</tr><tr>
<td>
<p><code><a href="WHONET.html">WHONET</a></code> </p>
</td>

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@ -81,7 +81,7 @@ This page contains a section for every lifecycle (with text borrowed from the af
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1.9000</span>
</span>
</div>
@ -117,6 +117,13 @@ This page contains a section for every lifecycle (with text borrowed from the af
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress-alt"></span>
Conduct Principal Component Analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
@ -236,7 +243,7 @@ This page contains a section for every lifecycle (with text borrowed from the af
<p><img src='figures/lifecycle_experimental.svg' style=margin-bottom:5px /> <br />
The lifecycle of this function is <strong>experimental</strong>. An experimental function is in the very early stages of development. The unlying code might be changing frequently as we rapidly iterate and explore variations in search of the best fit. Experimental functions might be removed without deprecation, so you are generally best off waiting until a function is more mature before you use it in production code. Experimental functions will not be included in releases we submit to CRAN.</p>
The lifecycle of this function is <strong>experimental</strong>. An experimental function is in the very early stages of development. The unlying code might be changing frequently as we rapidly iterate and explore variations in search of the best fit. Experimental functions might be removed without deprecation, so you are generally best off waiting until a function is more mature before you use it in production code. Experimental functions will not be included in releases we submit to CRAN, since they have not yet matured enough.</p>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing lifecycle</h2>

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@ -0,0 +1,362 @@
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<h1>Principal Component Analysis (for AMR)</h1>
<div class="hidden name"><code>pca.Rd</code></div>
</div>
<div class="ref-description">
<p>Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels.</p>
</div>
<pre class="usage"><span class='co'># S3 method for data.frame</span>
<span class='fu'><a href='https://rdrr.io/r/stats/prcomp.html'>prcomp</a></span>(
<span class='no'>x</span>,
<span class='no'>...</span>,
<span class='kw'>retx</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>center</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>scale.</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>tol</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>rank.</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
)
<span class='fu'>pca</span>(<span class='no'>x</span>, <span class='no'>...</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>x</th>
<td><p>a <a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> containing numeric columns</p></td>
</tr>
<tr>
<th>...</th>
<td><p>columns of <code>x</code> to be selected for PCA</p></td>
</tr>
<tr>
<th>retx</th>
<td><p>a logical value indicating whether the rotated variables
should be returned.</p></td>
</tr>
<tr>
<th>center</th>
<td><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></td>
</tr>
<tr>
<th>scale.</th>
<td><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'>scale</a></code>.</p></td>
</tr>
<tr>
<th>tol</th>
<td><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><a href='https://rdrr.io/r/base/Extremes.html'>min(dim(x))</a></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></td>
</tr>
<tr>
<th>rank.</th>
<td><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></td>
</tr>
</table>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<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 R function <code><a href='https://rdrr.io/r/stats/prcomp.html'>prcomp()</a></code>.</p>
<p>The result of the <code>pca()</code> function is a <code><a href='https://rdrr.io/r/stats/prcomp.html'>prcomp</a></code> 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>
<h2 class="hasAnchor" id="experimental-lifecycle"><a class="anchor" href="#experimental-lifecycle"></a>Experimental lifecycle</h2>
<p><img src='figures/lifecycle_experimental.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>experimental</strong>. An experimental function is in the very early stages of development. The unlying code might be changing frequently as we rapidly iterate and explore variations in search of the best fit. Experimental functions might be removed without deprecation, so you are generally best off waiting until a function is more mature before you use it in production code. Experimental functions will not be included in releases we submit to CRAN, since they have not yet matured enough.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'># `example_isolates` is a dataset available in the AMR package.</span>
<span class='co'># See ?example_isolates.</span>
<span class='co'># calculate the resistance per group first</span>
<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>dplyr</span>)
<span class='no'>resistance_data</span> <span class='kw'>&lt;-</span> <span class='no'>example_isolates</span> <span class='kw'>%&gt;%</span>
<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>
<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'>%&gt;%</span> <span class='co'># and genus as we do here</span>
<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>
<span class='co'># now conduct PCA for certain antimicrobial agents</span>
<span class='no'>pca_result</span> <span class='kw'>&lt;-</span> <span class='no'>resistance_data</span> <span class='kw'>%&gt;%</span>
<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>)
<span class='no'>pca_result</span>
<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>pca_result</span>)
<span class='fu'><a href='https://rdrr.io/r/stats/biplot.html'>biplot</a></span>(<span class='no'>pca_result</span>)
<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></pre>
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<h2>Contents</h2>
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<li><a href="#arguments">Arguments</a></li>
<li><a href="#details">Details</a></li>
<li><a href="#experimental-lifecycle">Experimental lifecycle</a></li>
<li><a href="#examples">Examples</a></li>
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