AMR/docs/reference/pca.html

285 lines
17 KiB
HTML
Raw Normal View History

2020-03-07 21:48:21 +01:00
<!DOCTYPE html>
2021-12-12 11:07:02 +01:00
<!-- 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=1.0"><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"><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/flatly/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="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 --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- 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"><!-- 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><!-- 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><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><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="@univgroningen"><!-- 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]>
2020-03-07 21:48:21 +01:00
<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
<![endif]--></head><body data-spy="scroll" data-target="#toc">
2020-03-07 21:48:21 +01:00
<div class="container template-reference-topic">
<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
2020-03-07 21:48:21 +01:00
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
2022-05-11 10:10:31 +02:00
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1.9007</span>
2020-03-07 21:48:21 +01:00
</span>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav"><li>
2020-03-07 21:48:21 +01:00
<a href="../index.html">
<span class="fa fa-home"></span>
2020-03-07 21:48:21 +01:00
Home
</a>
</li>
<li class="dropdown">
2022-05-11 10:10:31 +02:00
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
<span class="fa fa-question-circle"></span>
2020-03-07 21:48:21 +01:00
How to
<span class="caret"></span>
</a>
<ul class="dropdown-menu" role="menu"><li>
2020-03-07 21:48:21 +01:00
<a href="../articles/AMR.html">
<span class="fa fa-directions"></span>
2020-03-07 21:48:21 +01:00
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
2020-03-07 21:48:21 +01:00
Predict antimicrobial resistance
</a>
</li>
2020-08-21 11:40:13 +02:00
<li>
<a href="../articles/datasets.html">
<span class="fa fa-database"></span>
2020-08-21 11:40:13 +02:00
Data sets for download / own use
2020-08-21 11:40:13 +02:00
</a>
</li>
2020-03-07 21:48:21 +01:00
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
2020-03-07 21:48:21 +01:00
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
2020-03-07 21:48:21 +01:00
Determine multi-drug resistance (MDR)
</a>
</li>
<li>
<a href="../articles/WHONET.html">
<span class="fa fa-globe-americas"></span>
2020-03-07 21:48:21 +01:00
Work with WHONET data
</a>
</li>
<li>
<a href="../articles/SPSS.html">
<span class="fa fa-file-upload"></span>
2020-03-07 21:48:21 +01:00
Import data from SPSS/SAS/Stata
</a>
</li>
<li>
<a href="../articles/EUCAST.html">
<span class="fa fa-exchange-alt"></span>
2020-03-07 21:48:21 +01:00
Apply EUCAST rules
</a>
</li>
<li>
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
2020-03-07 21:48:21 +01:00
Get properties of a microorganism
</a>
</li>
<li>
<a href="../reference/ab_property.html">
<span class="fa fa-capsules"></span>
2020-03-07 21:48:21 +01:00
Get properties of an antibiotic
</a>
</li>
<li>
<a href="../articles/benchmarks.html">
<span class="fa fa-shipping-fast"></span>
2020-03-07 21:48:21 +01:00
Other: benchmarks
</a>
</li>
</ul></li>
2020-03-07 21:48:21 +01:00
<li>
2020-07-28 18:39:57 +02:00
<a href="../reference/index.html">
<span class="fa fa-book-open"></span>
2020-03-07 21:48:21 +01:00
Manual
</a>
</li>
<li>
<a href="../authors.html">
<span class="fa fa-users"></span>
2020-03-07 21:48:21 +01:00
Authors
</a>
</li>
<li>
2020-07-28 18:39:57 +02:00
<a href="../news/index.html">
2021-05-12 18:15:03 +02:00
<span class="far fa-newspaper"></span>
2020-03-07 21:48:21 +01:00
Changelog
</a>
</li>
</ul><ul class="nav navbar-nav navbar-right"><li>
<a href="https://github.com/msberends/AMR" class="external-link">
2021-05-12 18:15:03 +02:00
<span class="fab fa-github"></span>
2020-03-07 21:48:21 +01:00
Source Code
</a>
</li>
</ul></div><!--/.nav-collapse -->
2020-03-07 21:48:21 +01:00
</div><!--/.container -->
</div><!--/.navbar -->
</header><div class="row">
2020-03-07 21:48:21 +01:00
<div class="col-md-9 contents">
<div class="page-header">
<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>
2020-03-07 21:48:21 +01:00
<div class="hidden name"><code>pca.Rd</code></div>
</div>
<div class="ref-description">
2020-03-08 11:18:59 +01:00
<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>
2020-03-07 21:48:21 +01:00
</div>
<div id="ref-usage">
<div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">pca</span><span class="op">(</span>
<span class="va">x</span>,
<span class="va">...</span>,
retx <span class="op">=</span> <span class="cn">TRUE</span>,
center <span class="op">=</span> <span class="cn">TRUE</span>,
scale. <span class="op">=</span> <span class="cn">TRUE</span>,
tol <span class="op">=</span> <span class="cn">NULL</span>,
rank. <span class="op">=</span> <span class="cn">NULL</span>
<span class="op">)</span></code></pre></div>
</div>
<div id="arguments">
<h2>Arguments</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
2020-03-07 21:48:21 +01:00
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
2020-03-07 21:48:21 +01:00
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
2020-03-07 21:48:21 +01:00
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
2020-03-07 21:48:21 +01:00
<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.,
2020-03-07 21:48:21 +01:00
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 id="value">
<h2>Value</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 id="details">
<h2>Details</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 id="stable-lifecycle">
<h2>Stable Lifecycle</h2>
2020-03-07 21:48:21 +01:00
<p><img src="figures/lifecycle_stable.svg" style='margin-bottom:"5"'><br>
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>
2022-03-10 19:33:25 +01:00
<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>
</div>
<div id="read-more-on-our-website-">
<h2>Read more on Our Website!</h2>
<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>
</div>
2020-03-07 21:48:21 +01:00
<div id="ref-examples">
<h2>Examples</h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># `example_isolates` is a data set available in the AMR package.</span>
<span class="co"># See ?example_isolates.</span>
<span class="co"># \donttest{</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 class="co"># calculate the resistance per group first </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 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>
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">%&gt;%</a></span> <span class="co"># and genus as we do here;</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.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="co"># then get resistance of all drugs</span>
<span class="co"># now conduct PCA for certain antimicrobial agents</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 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 class="va">pca_result</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 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 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>
<span class="op">}</span>
<span class="co"># }</span>
</code></pre></div>
</div>
2020-03-07 21:48:21 +01:00
</div>
2020-04-13 21:09:56 +02:00
<div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
<nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
</nav></div>
2020-03-07 21:48:21 +01:00
</div>
<footer><div class="copyright">
2022-03-27 09:37:55 +02:00
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
2020-03-07 21:48:21 +01:00
</div>
<div class="pkgdown">
2022-05-11 10:10:31 +02:00
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p>
2020-03-07 21:48:21 +01:00
</div>
</footer></div>
2020-03-07 21:48:21 +01:00
</body></html>
2020-03-07 21:48:21 +01:00