update for issue #15

Author: m-clark

README.md

@@ -1,5 +1,5 @@
 # mixed-models-with-R
 
-This document provides an introduction to conducting mixed models. It uses <span class="pack">lme4</span> as the primary tool, but demonstrates others.  Topics include random intercept and slope models, discussion of crossed vs. nested random effects, some common extensions (e.g. generalized linear mixed models), Bayesian tools, and other models that deal with dependency in the data.
+This document provides an introduction to mixed models. It uses <span class="pack">lme4</span> as the primary tool, but demonstrates others.  Topics include random intercept and slope models, discussion of crossed vs. nested random effects, some common extensions (e.g. generalized linear mixed models), Bayesian tools, and other models that deal with dependency in the data.
 
 [Link to doc](http://m-clark.github.io/mixed-models-with-R/).

docs/appendix.html

@@ -6,7 +6,7 @@
   <meta http-equiv="X-UA-Compatible" content="IE=edge" />
   <title>Appendix | Mixed Models with R</title>
   <meta name="description" content="This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mixed models, Bayesian approaches, and realms beyond." />
-  <meta name="generator" content="bookdown 0.20.2 and GitBook 2.6.7" />
+  <meta name="generator" content="bookdown 0.21 and GitBook 2.6.7" />
 
   <meta property="og:title" content="Appendix | Mixed Models with R" />
   <meta property="og:type" content="book" />
@@ -23,7 +23,6 @@
 
 
 
-<meta name="date" content="2020-10-19" />
 
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@@ -32,7 +31,7 @@
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 <link rel="prev" href="supplemental.html"/>
 
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@@ -50,7 +49,9 @@
 
 
 
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@@ -368,17 +369,17 @@ <h3>R</h3>
 <div id="python" class="section level3">
 <h3>Python</h3>
 <p>In Python, the <a href="http://www.statsmodels.org/stable/mixed_linear.html">statsmodels</a> module has basic capabilities for mixed models, but not too many frills relatively speaking. CSCAR director Kerby Shedden has been part of this specific development. You can see his notes from the Data Science Skills Series <a href="https://github.com/kshedden/Statsmodels-MixedLM">here</a>. The following provides an example.</p>
-<div class="sourceCode" id="cb145"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb145-1"><a href="appendix.html#cb145-1" aria-hidden="true"></a>pycheck =<span class="st"> </span><span class="kw">ifelse</span>(<span class="kw">grepl</span>(<span class="st">&#39;Windows&#39;</span>, <span class="kw">sessionInfo</span>()<span class="op">$</span>running), </span>
-<span id="cb145-2"><a href="appendix.html#cb145-2" aria-hidden="true"></a>                 <span class="st">&#39;C:/ProgramData/Anaconda3&#39;</span>, </span>
-<span id="cb145-3"><a href="appendix.html#cb145-3" aria-hidden="true"></a>                 <span class="st">&#39;/Users/micl/opt/anaconda3/bin/python&#39;</span>)</span></code></pre></div>
-<div class="sourceCode" id="cb146"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb146-1"><a href="appendix.html#cb146-1" aria-hidden="true"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
-<span id="cb146-2"><a href="appendix.html#cb146-2" aria-hidden="true"></a><span class="im">import</span> statsmodels.api <span class="im">as</span> sm</span>
-<span id="cb146-3"><a href="appendix.html#cb146-3" aria-hidden="true"></a><span class="im">import</span> statsmodels.formula.api <span class="im">as</span> smf</span>
-<span id="cb146-4"><a href="appendix.html#cb146-4" aria-hidden="true"></a></span>
-<span id="cb146-5"><a href="appendix.html#cb146-5" aria-hidden="true"></a>gpa <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/gpa.csv&#39;</span>)</span>
-<span id="cb146-6"><a href="appendix.html#cb146-6" aria-hidden="true"></a></span>
-<span id="cb146-7"><a href="appendix.html#cb146-7" aria-hidden="true"></a>gpa_py_mixed <span class="op">=</span> smf.mixedlm(<span class="st">&quot;gpa ~ occasion&quot;</span>, gpa, groups<span class="op">=</span>gpa[<span class="st">&quot;student&quot;</span>]).fit()</span>
-<span id="cb146-8"><a href="appendix.html#cb146-8" aria-hidden="true"></a><span class="bu">print</span>(gpa_py_mixed.summary())</span></code></pre></div>
+<div class="sourceCode" id="cb149"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb149-1"><a href="appendix.html#cb149-1" aria-hidden="true" tabindex="-1"></a>pycheck <span class="ot">=</span> <span class="fu">ifelse</span>(<span class="fu">grepl</span>(<span class="st">&#39;Windows&#39;</span>, <span class="fu">sessionInfo</span>()<span class="sc">$</span>running), </span>
+<span id="cb149-2"><a href="appendix.html#cb149-2" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&#39;C:/ProgramData/Anaconda3&#39;</span>, </span>
+<span id="cb149-3"><a href="appendix.html#cb149-3" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&#39;/Users/micl/opt/anaconda3/bin/python&#39;</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb150"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb150-1"><a href="appendix.html#cb150-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
+<span id="cb150-2"><a href="appendix.html#cb150-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> statsmodels.api <span class="im">as</span> sm</span>
+<span id="cb150-3"><a href="appendix.html#cb150-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> statsmodels.formula.api <span class="im">as</span> smf</span>
+<span id="cb150-4"><a href="appendix.html#cb150-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb150-5"><a href="appendix.html#cb150-5" aria-hidden="true" tabindex="-1"></a>gpa <span class="op">=</span> pd.read_csv(<span class="st">&#39;data/gpa.csv&#39;</span>)</span>
+<span id="cb150-6"><a href="appendix.html#cb150-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb150-7"><a href="appendix.html#cb150-7" aria-hidden="true" tabindex="-1"></a>gpa_py_mixed <span class="op">=</span> smf.mixedlm(<span class="st">&quot;gpa ~ occasion&quot;</span>, gpa, groups<span class="op">=</span>gpa[<span class="st">&quot;student&quot;</span>]).fit()</span>
+<span id="cb150-8"><a href="appendix.html#cb150-8" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(gpa_py_mixed.summary())</span></code></pre></div>
 <pre><code>
              Mixed Linear Model Regression Results
 =======================================================