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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=1.0"><title>Plot stacked ggplot2 graphs with the Variable Importance for the
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Projection (VIP) scores, mean replicate spectra (absorbance) per sample_id,
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and the preprocessed spectra. — plot_pls_vip • simplerspec</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" 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><meta property="og:title" content="Plot stacked ggplot2 graphs with the Variable Importance for the
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Projection (VIP) scores, mean replicate spectra (absorbance) per sample_id,
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and the preprocessed spectra. — plot_pls_vip"><meta property="og:description" content="Plot stacked ggplot2 graphs of VIP for the final
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PLS regression model output of the calibration (training) data set for the
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final number of components, raw (replicate mean) spectra, and preprocessed
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spectra. Regions with VIP &gt; 1 are highlighted across the stacked graphs
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in beige colour rectangles. VIP calculation is implemented as described in
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Chong, I.-G., and Jun, C.-H. (2005). Performance of some variable selection
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methods when multicollinearity is present. Chemometrics and Intelligent
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Laboratory Systems, 78(1--2), 103--112. https://doi.org/10.1016/j.chemolab.2004.12.011"><meta name="robots" content="noindex"><!-- 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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</header><div class="row">
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<div class="page-header">
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<h1>Plot stacked ggplot2 graphs with the Variable Importance for the
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Projection (VIP) scores, mean replicate spectra (absorbance) per sample_id,
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and the preprocessed spectra.</h1>
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<small class="dont-index">Source: <a href="https://github.com/philipp-baumann/simplerspec/blob/HEAD/R/pls-vip.R" class="external-link"><code>R/pls-vip.R</code></a></small>
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<div class="hidden name"><code>plot_pls_vip.Rd</code></div>
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</div>
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<div class="ref-description">
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<p>Plot stacked ggplot2 graphs of VIP for the final
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PLS regression model output of the calibration (training) data set for the
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final number of components, raw (replicate mean) spectra, and preprocessed
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spectra. Regions with VIP > 1 are highlighted across the stacked graphs
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in beige colour rectangles. VIP calculation is implemented as described in
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Chong, I.-G., and Jun, C.-H. (2005). Performance of some variable selection
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methods when multicollinearity is present. Chemometrics and Intelligent
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Laboratory Systems, 78(1--2), 103--112. https://doi.org/10.1016/j.chemolab.2004.12.011</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><span class="fu">plot_pls_vip</span><span class="op">(</span><span class="va">mout</span>, y1 <span class="op">=</span> <span class="st">"spc_mean"</span>, y2 <span class="op">=</span> <span class="st">"spc_pre"</span>,</span>
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<span> by <span class="op">=</span> <span class="st">"sample_id"</span>,</span>
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<span> xlab <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/expression.html" class="external-link">expression</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Wavenumber ["</span>, <span class="va">cm</span><span class="op">^</span><span class="op">-</span><span class="fl">1</span>, <span class="st">"]"</span><span class="op">)</span><span class="op">)</span>,</span>
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<span> ylab1 <span class="op">=</span> <span class="st">"Absorbance"</span>, ylab2 <span class="op">=</span> <span class="st">"Preprocessed Abs."</span>,</span>
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<span> alpha <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span></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>mout</dt>
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<dd><p>Model output list that is returned from
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<code><a href="fit_pls.html">simplerspec::fit_pls()</a></code>. This object contains a nested list with
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the <code><a href="https://rdrr.io/pkg/caret/man/train.html" class="external-link">caret::train()</a></code> object (class <code>train</code>), based on which
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VIPs at finally selected number of PLS components are computed.</p></dd>
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<dt>y1</dt>
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<dd><p>Character vector of list-column name in
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<code>mout$data$calibration</code>, where spectra for bottom graph are extracted.
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Default is <code>"spc_mean"</code>, which plots the mean calibration spectra after
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resampling.</p></dd>
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<dt>y2</dt>
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<dd><p>Character string of list-column name in
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<code>mout$data$calibration</code>, where spectra for bottom graph are extracted.
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Default is <code>"spc_pre"</code>, which plots the preprocessed calibration
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spectra after resampling.</p></dd>
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<dt>by</dt>
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<dd><p>Character string that is used to assign spectra to the same group
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and therefore ensures that all spectra are plotted with the same colour.
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Default is <code>"sample_id"</code></p></dd>
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<dt>xlab</dt>
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<dd><p>Character string of X axis title for shared x axis of stacked
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graphs. Default is <code>expression(paste("Wavenumber [", cm^-1, "]"))</code></p></dd>
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<dt>ylab1</dt>
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<dd><p>Y axis title of bottom spectrum. Default is <code>"Absorbance"</code>.</p></dd>
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<dt>ylab2</dt>
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<dd><p>Y axis title of bottom spectrum. Default is
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<code>"Preprocessed Abs."</code>.</p></dd>
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<dt>alpha</dt>
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<dd><p>Double between 0 and 1 that defines transparency of spectra
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lines in returned graph (ggplot plot object).</p></dd>
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</dl></div>
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