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<h1>Principal Component Analysis (for AMR)</h1>
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<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>
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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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<div id="ref-usage">
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<div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">pca</span><span class="op">(</span>
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<span class="va">x</span>,
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<span class="va">...</span>,
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retx <span class="op">=</span> <span class="cn">TRUE</span>,
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center <span class="op">=</span> <span class="cn">TRUE</span>,
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scale. <span class="op">=</span> <span class="cn">TRUE</span>,
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tol <span class="op">=</span> <span class="cn">NULL</span>,
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rank. <span class="op">=</span> <span class="cn">NULL</span>
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<span class="op">)</span></code></pre></div>
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</div>
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<div id="arguments">
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<h2>Arguments</h2>
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<dl><dt>x</dt>
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<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>
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<dt>...</dt>
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<dd><p>columns of <code>x</code> to be selected for PCA, can be unquoted since it supports quasiquotation.</p></dd>
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<dt>retx</dt>
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<dd><p>a logical value indicating whether the rotated variables
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should be returned.</p></dd>
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<dt>center</dt>
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<dd><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></dd>
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<dt>scale.</dt>
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<dd><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" class="external-link">scale</a></code>.</p></dd>
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<dt>tol</dt>
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<dd><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>min(dim(x))</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></dd>
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<dt>rank.</dt>
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<dd><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></dd>
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</dl></div>
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<div id="value">
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<h2>Value</h2>
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<p>An object of classes pca and <a href="https://rdrr.io/r/stats/prcomp.html" class="external-link">prcomp</a></p>
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</div>
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<div id="details">
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<h2>Details</h2>
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<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>
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<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>
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</div>
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<div id="stable-lifecycle">
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<h2>Stable Lifecycle</h2>
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<p><img src="figures/lifecycle_stable.svg" style='margin-bottom:"5"'><br>
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The <a href="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>
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<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>
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</div>
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<div id="read-more-on-our-website-">
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<h2>Read more on Our Website!</h2>
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<p>On our website <a href="https://msberends.github.io/AMR/">https://msberends.github.io/AMR/</a> you can find <a href="https://msberends.github.io/AMR/articles/AMR.html">a comprehensive tutorial</a> about how to conduct AMR data analysis, the <a href="https://msberends.github.io/AMR/reference/">complete documentation of all functions</a> and <a href="https://msberends.github.io/AMR/articles/WHONET.html">an example analysis using WHONET data</a>.</p>
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</div>
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<div id="ref-examples">
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<h2>Examples</h2>
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<div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># `example_isolates` is a data set available in the AMR package.</span>
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<span class="co"># See ?example_isolates.</span>
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<span class="co"># \donttest{</span>
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<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>
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<span class="co"># calculate the resistance per group first </span>
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<span class="va">resistance_data</span> <span class="op"><-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</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>
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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 class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></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" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</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="va">pca_result</span> <span class="op"><-</span> <span class="va">resistance_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span>
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<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>
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<span class="va">pca_result</span>
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<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>
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<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>
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<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="co"># a new and convenient plot function</span>
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<span class="op">}</span>
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<span class="co"># }</span>
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</code></pre></div>
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