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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, shrink-to-fit=no"><title>PCA Biplot with ggplot2 — ggplot_pca • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="PCA Biplot with ggplot2 — ggplot_pca"><meta name="description" content="Produces a ggplot2 variant of a so-called biplot for PCA (principal component analysis), but is more flexible and more appealing than the base R biplot() function."><meta property="og:description" content="Produces a ggplot2 variant of a so-called biplot for PCA (principal component analysis), but is more flexible and more appealing than the base R biplot() function."><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
<!-- 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, shrink-to-fit=no"><title>PCA Biplot with ggplot2 — ggplot_pca • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="PCA Biplot with ggplot2 — ggplot_pca"><meta name="description" content="Produces a ggplot2 variant of a so-called biplot for PCA (principal component analysis), but is more flexible and more appealing than the base R biplot() function."><meta property="og:description" content="Produces a ggplot2 variant of a so-called biplot for PCA (principal component analysis), but is more flexible and more appealing than the base R biplot() function."><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9057</small>
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@@ -55,13 +55,30 @@
<div class="section level2">
<h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ggplot_pca</span><span class="op">(</span><span class="va">x</span>, choices <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">2</span>, scale <span class="op">=</span> <span class="fl">1</span>, pc.biplot <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> labels <span class="op">=</span> <span class="cn">NULL</span>, labels_textsize <span class="op">=</span> <span class="fl">3</span>, labels_text_placement <span class="op">=</span> <span class="fl">1.5</span>,</span>
<span> groups <span class="op">=</span> <span class="cn">NULL</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span>, ellipse_prob <span class="op">=</span> <span class="fl">0.68</span>,</span>
<span> ellipse_size <span class="op">=</span> <span class="fl">0.5</span>, ellipse_alpha <span class="op">=</span> <span class="fl">0.5</span>, points_size <span class="op">=</span> <span class="fl">2</span>,</span>
<span> points_alpha <span class="op">=</span> <span class="fl">0.25</span>, arrows <span class="op">=</span> <span class="cn">TRUE</span>, arrows_colour <span class="op">=</span> <span class="st">"darkblue"</span>,</span>
<span> arrows_size <span class="op">=</span> <span class="fl">0.5</span>, arrows_textsize <span class="op">=</span> <span class="fl">3</span>, arrows_textangled <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> arrows_alpha <span class="op">=</span> <span class="fl">0.75</span>, base_textsize <span class="op">=</span> <span class="fl">10</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ggplot_pca</span><span class="op">(</span></span>
<span> <span class="va">x</span>,</span>
<span> choices <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">2</span>,</span>
<span> scale <span class="op">=</span> <span class="fl">1</span>,</span>
<span> pc.biplot <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> labels <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> labels_textsize <span class="op">=</span> <span class="fl">3</span>,</span>
<span> labels_text_placement <span class="op">=</span> <span class="fl">1.5</span>,</span>
<span> groups <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> ellipse <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> ellipse_prob <span class="op">=</span> <span class="fl">0.68</span>,</span>
<span> ellipse_size <span class="op">=</span> <span class="fl">0.5</span>,</span>
<span> ellipse_alpha <span class="op">=</span> <span class="fl">0.5</span>,</span>
<span> points_size <span class="op">=</span> <span class="fl">2</span>,</span>
<span> points_alpha <span class="op">=</span> <span class="fl">0.25</span>,</span>
<span> arrows <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> arrows_colour <span class="op">=</span> <span class="st">"darkblue"</span>,</span>
<span> arrows_size <span class="op">=</span> <span class="fl">0.5</span>,</span>
<span> arrows_textsize <span class="op">=</span> <span class="fl">3</span>,</span>
<span> arrows_textangled <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> arrows_alpha <span class="op">=</span> <span class="fl">0.75</span>,</span>
<span> base_textsize <span class="op">=</span> <span class="fl">10</span>,</span>
<span> <span class="va">...</span></span>
<span><span class="op">)</span></span></code></pre></div>
</div>
<div class="section level2">
@@ -96,9 +113,8 @@
<dt id="arg-pc-biplot">pc.biplot<a class="anchor" aria-label="anchor" href="#arg-pc-biplot"></a></dt>
<dd><p>If true, use what <cite></cite><a href="#reference+biplot.princomp.Rd+R+3AGabriel+3A1971" class="citation">Gabriel (1971)</a> refers to as a
“principal component biplot”,
with <code>lambda = 1</code> and observations scaled up by sqrt(n) and
<dd><p>If true, use what Gabriel (1971) refers to as a "principal component
biplot", with <code>lambda = 1</code> and observations scaled up by sqrt(n) and
variables scaled down by sqrt(n). Then inner products between
variables approximate covariances and distances between observations
approximate Mahalanobis distance.</p></dd>