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Bootstrapping.html
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<h1 class="title toc-ignore">Bootstrap and Simulation for Nonlinear Models</h1>
<h4 class="author">Fernando Miguez</h4>
<h4 class="date">2020-04-23</h4>
<p>Preliminaries: first, we need to load required libraries.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">library</span>(nlraa)</a>
<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw">library</span>(car)</a></code></pre></div>
<pre><code>## Loading required package: carData</code></pre>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw">library</span>(nlme)</a></code></pre></div>
<div id="bootstrapping" class="section level1">
<h1>Bootstrapping</h1>
<p>The bootstrap is a technique in statistics which consists of resampling the observed data in order to create an empirical distribution of some statistic (Fox and Weisberg, 2012, 2017). This is often done to evaluate the assumptions of the model or to derive empirical distributions which are difficult to approximate. For nonlinear models, bootstrapping can be useful because often questions arise that a typical analysis does not answer. In many instances the distributions of parameters from a nonlinear model are in fact normal and standard approximations work well, in other instances, however, this is not the case.</p>
<p>In the following sections I will illustrate the use of the bootstrap for nonlinear model (nls), generalized nonlinear models (gnls) and nonlinear mixed models (nlme). In another section I will illustrate the ability to simulate from nonlinear (mixed) models</p>
<div id="nonlinear-models" class="section level2">
<h2>Nonlinear models</h2>
<p>The first type of models illustrated here are nonlinear models for which we make standard assumptions for error distributions and there are no random effects.</p>
<div id="simple-example" class="section level3">
<h3>Simple example</h3>
<p>As a simple example, we can use the dataset ‘barley’ in the ‘nlraa’ package. This represents the response of barley yield to different doses of fertilizer over several seasons. We will ignore the effect of ‘year’ in this section.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw">data</span>(barley, <span class="dt">package =</span> <span class="st">"nlraa"</span>)</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"></a>
<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co">## Quick visualization</span></a>
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="kw">ggplot</span>(<span class="dt">data =</span> barley, <span class="kw">aes</span>(<span class="dt">x =</span> NF, <span class="dt">y =</span> yield)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>()</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>We can fit a very simple model known as the linear-plateau.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="co">## Linear-plateau model</span></a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co">## The function SSlinp is in the 'nlraa' package</span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3">fit.lp <-<span class="st"> </span><span class="kw">nls</span>(yield <span class="op">~</span><span class="st"> </span><span class="kw">SSlinp</span>(NF, a, b, xs), <span class="dt">data =</span> barley)</a>
<a class="sourceLine" id="cb5-4" data-line-number="4"></a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="co">## Visualize data and fit</span></a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="kw">ggplot</span>(<span class="dt">data =</span> barley, <span class="kw">aes</span>(<span class="dt">x =</span> NF, <span class="dt">y =</span> yield)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="st"> </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">y =</span> <span class="kw">fitted</span>(fit.lp)))</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>At this point several questions might arise:</p>
<ol style="list-style-type: decimal">
<li>What are the confidence intervals for the model parameters?</li>
<li>How do we describe the uncertainty around the fitted values of the model?</li>
<li>What is the estimate and confidence interval for the asymptote?</li>
</ol>
</div>
<div id="confidence-interval-for-model-parameters" class="section level3">
<h3>Confidence interval for model parameters</h3>
<p>The confidence intervals for the model parameters can be derived using a method called profiling. The generic function ‘confint’ can be used which invokes ‘confint.nls’ from the ‘MASS’ package.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw">confint</span>(fit.lp)</a></code></pre></div>
<pre><code>## Waiting for profiling to be done...</code></pre>
<pre><code>## 2.5% 97.5%
## a 104.821725 167.73660
## b 19.937411 38.00267
## xs 5.849474 12.23099</code></pre>
<p>For nonlinear models this method is preferred to simple confidence intervals that would directly use the standard error of the model parameters. In many cases, there is good agreement between the two, but this is not always the case and it can be informative to compare both methods. Those standard errors can be obtained using the ‘summary’ function, along with hypothesis testing.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1"><span class="kw">summary</span>(fit.lp)</a></code></pre></div>
<pre><code>##
## Formula: yield ~ SSlinp(NF, a, b, xs)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 137.067 16.179 8.472 1.83e-12 ***
## b 27.501 5.269 5.219 1.62e-06 ***
## xs 7.682 1.211 6.342 1.68e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 70.79 on 73 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 6.548e-16</code></pre>
<p>Some interesting plots can be produced which illustrate the symmetry (or lack thereof) of the distribution for the model parameters. Profile plots which show large departures from normality would indicate that the normal approximation for the confidence intervals might not be the best choice.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1"><span class="co">## For the intercept</span></a>
<a class="sourceLine" id="cb11-2" data-line-number="2"><span class="kw">plot</span>(<span class="kw">profile</span>(fit.lp, <span class="st">"a"</span>))</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1"><span class="co">## This one is fairly symetric and the normal approximation is reasonable</span></a>
<a class="sourceLine" id="cb12-2" data-line-number="2"><span class="kw">plot</span>(<span class="kw">profile</span>(fit.lp, <span class="st">"b"</span>))</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1"><span class="kw">plot</span>(<span class="kw">profile</span>(fit.lp, <span class="st">"xs"</span>))</a></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" data-line-number="1"><span class="co">## These last parameters are less symetrical</span></a></code></pre></div>
<p>Being able to see the whole profile for a parameter can be interesting in terms of understanding its behavior. For example, in this model, which has a ‘break-point’, we would not expect all the parameters to have identical shaped profiles and that is illustrated above for parameter ‘xs’.</p>
<p>In this case, bootstraping is an alternative to computing the confidence intervals and it can be used as a way to double-check the previous results, but it can also be used when profiling fails. A function which can perform bootstraping for nonlinear models is ‘Boot’ in the ‘car’ pacakge.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1">fit.lp.Bt <-<span class="st"> </span><span class="kw">Boot</span>(fit.lp)</a></code></pre></div>
<pre><code>## Loading required namespace: boot</code></pre>
<pre><code>##
## Number of bootstraps was 814 out of 999 attempted</code></pre>
<p>In this case, this function uses the base ‘boot’ package under the hood and it returns an object of that class. This also allows for other options such as defining a function as a combination of model parameters, and the use of more than one core to speed up computation. In this case, since we are also interested in the asymptote, which is ‘a + b * xs’, we can obtain confidence intervals for this parameter by doing the following.</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb18-1" data-line-number="1">fn <-<span class="st"> </span><span class="cf">function</span>(x) <span class="kw">coef</span>(x)[<span class="dv">1</span>] <span class="op">+</span><span class="st"> </span><span class="kw">coef</span>(x)[<span class="dv">2</span>] <span class="op">*</span><span class="st"> </span><span class="kw">coef</span>(x)[<span class="dv">3</span>]</a>
<a class="sourceLine" id="cb18-2" data-line-number="2">fit.lp.Bt.asymp <-<span class="st"> </span><span class="kw">Boot</span>(fit.lp, <span class="dt">f =</span> fn, <span class="dt">labels =</span> <span class="st">"asymptote"</span>)</a></code></pre></div>
<pre><code>##
## Number of bootstraps was 813 out of 999 attempted</code></pre>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" data-line-number="1"><span class="kw">confint</span>(fit.lp.Bt.asymp)</a></code></pre></div>
<pre><code>## Bootstrap bca confidence intervals
##
## 2.5 % 97.5 %
## asymptote 324.0106 380.8581</code></pre>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb22-1" data-line-number="1"><span class="kw">hist</span>(fit.lp.Bt.asymp)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>The bootstrap method takes a few seconds here, but it can be computationally much more demanding in larger problems since it re-fits the model many, many times. An alternative is to use the delta method, for which there is a function in the ‘car’ pacakge. The delta method has the advantage of being fast and the disadvantage that it makes the assumption of a normal distribution. In this case the lower bound for the confidence interval is similar to the one obtained with the bootstrap, but the upper bound for the confidence interval is somewhat higher using the bootstrap (381 vs. 370). Since the bootstrap method makes fewer assumptions it is probably the better one to report.</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb23-1" data-line-number="1">fit.lp.Dlt.asymp <-<span class="st"> </span><span class="kw">deltaMethod</span>(fit.lp, <span class="st">"a + b * xs"</span>)</a>
<a class="sourceLine" id="cb23-2" data-line-number="2">fit.lp.Dlt.asymp</a></code></pre></div>
<pre><code>## Estimate SE 2.5 % 97.5 %
## a + b * xs 348.326 11.484 325.817 370.84</code></pre>
<p>In order to answer our second question above related to the uncertainty around the fitted values we could plug-in the values from the bootstrap sampling and obtain different regression lines.</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" data-line-number="1"><span class="co">## The object 't' in the bootstrap run has </span></a>
<a class="sourceLine" id="cb25-2" data-line-number="2"><span class="co">## the parameter estimate values</span></a>
<a class="sourceLine" id="cb25-3" data-line-number="3"><span class="co">## First remove missing values</span></a>
<a class="sourceLine" id="cb25-4" data-line-number="4">fit.lp.Bt.prms <-<span class="st"> </span><span class="kw">na.omit</span>(fit.lp.Bt<span class="op">$</span>t)</a>
<a class="sourceLine" id="cb25-5" data-line-number="5"></a>
<a class="sourceLine" id="cb25-6" data-line-number="6">nrb <-<span class="st"> </span><span class="kw">length</span>(<span class="kw">unique</span>(barley<span class="op">$</span>NF))</a>
<a class="sourceLine" id="cb25-7" data-line-number="7">nrp <-<span class="st"> </span><span class="kw">nrow</span>(fit.lp.Bt.prms)</a>
<a class="sourceLine" id="cb25-8" data-line-number="8"></a>
<a class="sourceLine" id="cb25-9" data-line-number="9"><span class="co">## Set up an empty data.frame </span></a>
<a class="sourceLine" id="cb25-10" data-line-number="10">prd.dat <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">i =</span> <span class="kw">as.factor</span>(<span class="kw">rep</span>(<span class="dv">1</span><span class="op">:</span>nrp, <span class="dt">each =</span> nrb)), <span class="dt">NF =</span> <span class="kw">rep</span>(<span class="kw">unique</span>(barley<span class="op">$</span>NF), nrp), <span class="dt">prd =</span> <span class="ot">NA</span>)</a>
<a class="sourceLine" id="cb25-11" data-line-number="11"></a>
<a class="sourceLine" id="cb25-12" data-line-number="12"><span class="co">## A simple loop can be used to run the model multiple times</span></a>
<a class="sourceLine" id="cb25-13" data-line-number="13"><span class="cf">for</span>(i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span>nrp){</a>
<a class="sourceLine" id="cb25-14" data-line-number="14"> a.i <-<span class="st"> </span>fit.lp.Bt.prms[i,<span class="dv">1</span>]</a>
<a class="sourceLine" id="cb25-15" data-line-number="15"> b.i <-<span class="st"> </span>fit.lp.Bt.prms[i,<span class="dv">2</span>]</a>
<a class="sourceLine" id="cb25-16" data-line-number="16"> xs.i <-<span class="st"> </span>fit.lp.Bt.prms[i,<span class="dv">3</span>]</a>
<a class="sourceLine" id="cb25-17" data-line-number="17"> </a>
<a class="sourceLine" id="cb25-18" data-line-number="18"> prd.dat[<span class="kw">c</span>(<span class="dv">1</span> <span class="op">+</span><span class="st"> </span>(nrb<span class="op">*</span>(i <span class="op">-</span><span class="st"> </span><span class="dv">1</span>)))<span class="op">:</span><span class="kw">c</span>(i <span class="op">*</span><span class="st"> </span>nrb),<span class="dv">3</span>] <-<span class="st"> </span><span class="kw">linp</span>(<span class="kw">unique</span>(barley<span class="op">$</span>NF), a.i, b.i, xs.i)</a>
<a class="sourceLine" id="cb25-19" data-line-number="19">}</a>
<a class="sourceLine" id="cb25-20" data-line-number="20"></a>
<a class="sourceLine" id="cb25-21" data-line-number="21"><span class="co">## Plot the data with the original fit and the uncertainty</span></a>
<a class="sourceLine" id="cb25-22" data-line-number="22"><span class="kw">ggplot</span>() <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb25-23" data-line-number="23"><span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">data =</span> prd.dat, <span class="kw">aes</span>(<span class="dt">x =</span> NF, <span class="dt">y =</span> prd, <span class="dt">group =</span> i), </a>
<a class="sourceLine" id="cb25-24" data-line-number="24"> <span class="dt">color =</span> <span class="st">"gray"</span>, <span class="dt">alpha =</span> <span class="fl">0.2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-25" data-line-number="25"><span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">data =</span> barley, <span class="kw">aes</span>(<span class="dt">x =</span> NF, <span class="dt">y =</span> <span class="kw">fitted</span>(fit.lp))) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb25-26" data-line-number="26"><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">data =</span> barley, <span class="kw">aes</span>(<span class="dt">x =</span> NF, <span class="dt">y =</span> yield)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb25-27" data-line-number="27"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Yield"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Nitrogen rate"</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb25-28" data-line-number="28"><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Using results from Boot </span><span class="ch">\n</span><span class="st"> and plug-in into linp"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>The previous graph shows the black line with the original fit and the variability in the fitted values due to the resampling generated during the bootstrap process. The function ‘predict.nls’ at the moment ignores the arguments ‘se.fit’ and ‘interval’ which means that this functionality is not available in the base ‘stats’ package. (There is an alternative approach in the ‘propagate’ package - see references.)</p>
<p>So we could use the quantiles of ‘prd.dat’ object above to derive confidence intervals for the regression line. The previous example is an attempt to make it clear how the uncertainty could be displayed. Equivalently, we can use ‘Boot’ for this purpose, but with some effort manipulating the data.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1">fn2 <-<span class="st"> </span><span class="cf">function</span>(x) <span class="kw">predict</span>(x, <span class="dt">newdata =</span> <span class="kw">data.frame</span>(<span class="dt">NF =</span> <span class="dv">0</span><span class="op">:</span><span class="dv">14</span>))</a>
<a class="sourceLine" id="cb26-2" data-line-number="2">fit.lp.Bt2 <-<span class="st"> </span><span class="kw">Boot</span>(fit.lp, fn2)</a></code></pre></div>
<pre><code>##
## Number of bootstraps was 820 out of 999 attempted</code></pre>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb28-1" data-line-number="1">fttd <-<span class="st"> </span><span class="kw">na.omit</span>(fit.lp.Bt2<span class="op">$</span>t)</a>
<a class="sourceLine" id="cb28-2" data-line-number="2">prds <-<span class="st"> </span><span class="kw">c</span>(<span class="kw">t</span>(fttd))</a>
<a class="sourceLine" id="cb28-3" data-line-number="3">ndat <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">i =</span> <span class="kw">as.factor</span>(<span class="kw">rep</span>(<span class="dv">1</span><span class="op">:</span><span class="kw">nrow</span>(fttd), <span class="dt">each =</span> <span class="kw">ncol</span>(fttd))),</a>
<a class="sourceLine" id="cb28-4" data-line-number="4"> <span class="dt">NF =</span> <span class="kw">rep</span>(<span class="dv">0</span><span class="op">:</span><span class="dv">14</span>, <span class="kw">nrow</span>(fttd)))</a>
<a class="sourceLine" id="cb28-5" data-line-number="5">ndat<span class="op">$</span>prd <-<span class="st"> </span>prds</a>
<a class="sourceLine" id="cb28-6" data-line-number="6"></a>
<a class="sourceLine" id="cb28-7" data-line-number="7"><span class="co">## Essentially the same graph as the one above</span></a>
<a class="sourceLine" id="cb28-8" data-line-number="8"><span class="kw">ggplot</span>() <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb28-9" data-line-number="9"><span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">data =</span> ndat, <span class="kw">aes</span>(<span class="dt">x =</span> NF, <span class="dt">y =</span> prd, <span class="dt">group =</span> i), </a>
<a class="sourceLine" id="cb28-10" data-line-number="10"> <span class="dt">color =</span> <span class="st">"gray"</span>, <span class="dt">alpha =</span> <span class="fl">0.2</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb28-11" data-line-number="11"><span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">data =</span> barley, <span class="kw">aes</span>(<span class="dt">x =</span> NF, <span class="dt">y =</span> <span class="kw">fitted</span>(fit.lp))) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb28-12" data-line-number="12"><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">data =</span> barley, <span class="kw">aes</span>(<span class="dt">x =</span> NF, <span class="dt">y =</span> yield)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb28-13" data-line-number="13"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Yield"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Nitrogen rate"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="bootstrapping-generalized-nonlinear-models" class="section level2">
<h2>Bootstrapping generalized nonlinear models</h2>
<p>Implementing bootstrap for more complex models takes extra work. For this, I’m taking the approach of sampling from the vector of fixed parameters and also bootstrapping the standardized residuals for ‘gnls’ and ‘nlme’ objects. (This methodology can be improved in the future.) This takes advantage that we assume that the residuals are normally distributed.</p>
<p>As a first example, we can compare the bootstrapped confidence intervals with the ones obtained by ‘intervals’. Note: I’m running this only a few hundred times in the examples below for efficiency, but it is a good practice to run these a few thousand times.</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb29-1" data-line-number="1"><span class="kw">set.seed</span>(<span class="dv">101</span>)</a>
<a class="sourceLine" id="cb29-2" data-line-number="2"><span class="co">## Simplify the dataset to make the set up simpler</span></a>
<a class="sourceLine" id="cb29-3" data-line-number="3">barley2 <-<span class="st"> </span><span class="kw">subset</span>(barley, year <span class="op"><</span><span class="st"> </span><span class="dv">1974</span>)</a>
<a class="sourceLine" id="cb29-4" data-line-number="4"></a>
<a class="sourceLine" id="cb29-5" data-line-number="5">fit.lp.gnls2 <-<span class="st"> </span><span class="kw">gnls</span>(yield <span class="op">~</span><span class="st"> </span><span class="kw">SSlinp</span>(NF, a, b, xs), <span class="dt">data =</span> barley2)</a>
<a class="sourceLine" id="cb29-6" data-line-number="6"></a>
<a class="sourceLine" id="cb29-7" data-line-number="7"><span class="kw">intervals</span>(fit.lp.gnls2)</a></code></pre></div>
<pre><code>## Approximate 95% confidence intervals
##
## Coefficients:
## lower est. upper
## a 138.79981 176.800000 214.800186
## b 13.75248 27.603093 41.453706
## xs 3.64251 6.174127 8.705744
## attr(,"label")
## [1] "Coefficients:"
##
## Residual standard error:
## lower est. upper
## 25.50341 35.17935 56.67543</code></pre>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb31-1" data-line-number="1"><span class="co">## Compare this to the bootstrapping approach</span></a>
<a class="sourceLine" id="cb31-2" data-line-number="2">fit.lp.gnls2.bt <-<span class="st"> </span><span class="kw">boot_nlme</span>(fit.lp.gnls2, <span class="dt">R =</span> <span class="dv">200</span>)</a></code></pre></div>
<pre><code>## Number of times model fit did not converge 22 out of 200</code></pre>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb33-1" data-line-number="1"><span class="kw">summary</span>(fit.lp.gnls2.bt)</a></code></pre></div>
<pre><code>##
## Number of bootstrap replications R = 178
## original bootBias bootSE bootMed
## 1 176.8000 -1.881489 23.1895 174.7183
## 2 27.6031 1.095069 7.4412 28.4382
## 3 6.1741 -0.072778 1.3834 5.9537</code></pre>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb35-1" data-line-number="1"><span class="kw">confint</span>(fit.lp.gnls2.bt, <span class="dt">type =</span> <span class="st">"perc"</span>)</a></code></pre></div>
<pre><code>## Bootstrap percent confidence intervals
##
## 2.5 % 97.5 %
## 1 125.320034 224.479385
## 2 14.440280 42.522212
## 3 4.040724 9.591933</code></pre>
<p>The confidence intervals obtained by bootstrap are wider (as expected) than the ones obtained using intervals because they consider the uncertainty in the parameters of the nonlinear model. The next example shows a slightly more complex model in which we introduce the (fixed) effect of year for a subset of the data.</p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb37-1" data-line-number="1"><span class="kw">set.seed</span>(<span class="dv">101</span>)</a>
<a class="sourceLine" id="cb37-2" data-line-number="2">barley2<span class="op">$</span>year.f <-<span class="st"> </span><span class="kw">as.factor</span>(barley2<span class="op">$</span>year)</a>
<a class="sourceLine" id="cb37-3" data-line-number="3"></a>
<a class="sourceLine" id="cb37-4" data-line-number="4">cfs <-<span class="st"> </span><span class="kw">coef</span>(fit.lp.gnls2)</a>
<a class="sourceLine" id="cb37-5" data-line-number="5"></a>
<a class="sourceLine" id="cb37-6" data-line-number="6">fit.lp.gnls3 <-<span class="st"> </span><span class="kw">update</span>(fit.lp.gnls2, </a>
<a class="sourceLine" id="cb37-7" data-line-number="7"> <span class="dt">params =</span> <span class="kw">list</span>(a <span class="op">+</span><span class="st"> </span>b <span class="op">+</span><span class="st"> </span>xs <span class="op">~</span><span class="st"> </span>year.f),</a>
<a class="sourceLine" id="cb37-8" data-line-number="8"> <span class="dt">start =</span> <span class="kw">c</span>(cfs[<span class="dv">1</span>], <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, </a>
<a class="sourceLine" id="cb37-9" data-line-number="9"> cfs[<span class="dv">2</span>], <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>,</a>
<a class="sourceLine" id="cb37-10" data-line-number="10"> cfs[<span class="dv">3</span>], <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>))</a>
<a class="sourceLine" id="cb37-11" data-line-number="11"></a>
<a class="sourceLine" id="cb37-12" data-line-number="12"><span class="co">## This bootstraps the vector of parameters</span></a>
<a class="sourceLine" id="cb37-13" data-line-number="13">fit.lp.gnls3.bt <-<span class="st"> </span><span class="kw">boot_nlme</span>(fit.lp.gnls3, <span class="dt">R =</span> <span class="dv">300</span>)</a></code></pre></div>
<pre><code>## Number of times model fit did not converge 152 out of 300</code></pre>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb39-1" data-line-number="1"><span class="kw">confint</span>(fit.lp.gnls3.bt, <span class="dt">type =</span> <span class="st">"perc"</span>)</a></code></pre></div>
<pre><code>## Bootstrap percent confidence intervals
##
## 2.5 % 97.5 %
## 1 111.1375192 196.119143
## 2 -45.3383112 86.644389
## 3 -52.9212287 65.101962
## 4 -0.5059473 114.993794
## 5 14.3750448 41.574802
## 6 -20.9463834 15.697041
## 7 -17.7796470 16.955886
## 8 -18.1083851 16.965825
## 9 4.4735481 9.074686
## 10 -2.6628309 4.960814
## 11 -4.1799869 1.682101
## 12 -2.8113050 3.862596</code></pre>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb41-1" data-line-number="1"><span class="kw">hist</span>(fit.lp.gnls3.bt, <span class="dv">1</span>, <span class="dt">ci =</span> <span class="st">"perc"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>This is simply to illustrate the use of bootstrapping for a ‘gnls’ object, which is something that function ‘car::Boot’ does not seem to be able to handle (the deltaMethod function also fails for this type of model, because it cannot handle the names in the vector of coefficients which uses periods).</p>
</div>
<div id="bootstrapping-nonlinear-mixed-models" class="section level2">
<h2>Bootstrapping nonlinear mixed models</h2>
<p>For illustration, I will continue to use the barley example, but this time the model is fitted to each year individually and then a nonlinear mixed model which assumes a diagonal matrix for the random effects (for simplicity). In this case we want an esimtate of the confidence interval for the asymptote which is not an explicit parameter but rather a combination ‘a + b * xs’ of the three parameters.</p>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb42-1" data-line-number="1"><span class="kw">set.seed</span>(<span class="dv">101</span>)</a>
<a class="sourceLine" id="cb42-2" data-line-number="2">barley<span class="op">$</span>year.f <-<span class="st"> </span><span class="kw">as.factor</span>(barley<span class="op">$</span>year)</a>
<a class="sourceLine" id="cb42-3" data-line-number="3"></a>
<a class="sourceLine" id="cb42-4" data-line-number="4">barleyG <-<span class="st"> </span><span class="kw">groupedData</span>(yield <span class="op">~</span><span class="st"> </span>NF <span class="op">|</span><span class="st"> </span>year.f, <span class="dt">data =</span> barley)</a>
<a class="sourceLine" id="cb42-5" data-line-number="5"></a>
<a class="sourceLine" id="cb42-6" data-line-number="6">fitL.bar <-<span class="st"> </span><span class="kw">nlsList</span>(yield <span class="op">~</span><span class="st"> </span><span class="kw">SSlinp</span>(NF, a, b, xs), <span class="dt">data =</span> barleyG)</a>
<a class="sourceLine" id="cb42-7" data-line-number="7"></a>
<a class="sourceLine" id="cb42-8" data-line-number="8">fit.bar.nlme <-<span class="st"> </span><span class="kw">nlme</span>(fitL.bar, <span class="dt">random =</span> <span class="kw">pdDiag</span>(a <span class="op">+</span><span class="st"> </span>b <span class="op">+</span><span class="st"> </span>xs <span class="op">~</span><span class="st"> </span><span class="dv">1</span>))</a>
<a class="sourceLine" id="cb42-9" data-line-number="9"></a>
<a class="sourceLine" id="cb42-10" data-line-number="10"><span class="co">## Confidence intervals of the model fixed parameters</span></a>
<a class="sourceLine" id="cb42-11" data-line-number="11"><span class="kw">intervals</span>(fit.bar.nlme, <span class="dt">which =</span> <span class="st">"fixed"</span>)</a></code></pre></div>
<pre><code>## Approximate 95% confidence intervals
##
## Fixed effects:
## lower est. upper
## a 109.212647 134.663569 160.11449
## b 23.970656 27.939486 31.90832
## xs 6.823868 7.671139 8.51841
## attr(,"label")
## [1] "Fixed effects:"</code></pre>
<div class="sourceCode" id="cb44"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb44-1" data-line-number="1"><span class="co">## Function which computes the asymptote</span></a>
<a class="sourceLine" id="cb44-2" data-line-number="2">fna <-<span class="st"> </span><span class="cf">function</span>(x) <span class="kw">fixef</span>(x)[<span class="dv">1</span>] <span class="op">+</span><span class="st"> </span><span class="kw">fixef</span>(x)[<span class="dv">2</span>] <span class="op">*</span><span class="st"> </span><span class="kw">fixef</span>(x)[<span class="dv">3</span>]</a>
<a class="sourceLine" id="cb44-3" data-line-number="3"></a>
<a class="sourceLine" id="cb44-4" data-line-number="4"><span class="co">## Bootstrap the model for the asymptote</span></a>
<a class="sourceLine" id="cb44-5" data-line-number="5">fit.bar.nlme.bt <-<span class="st"> </span><span class="kw">boot_nlme</span>(fit.bar.nlme, <span class="dt">f =</span> fna, <span class="dt">R =</span> <span class="dv">200</span>)</a></code></pre></div>
<pre><code>## Number of times model fit did not converge 90 out of 200</code></pre>
<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb46-1" data-line-number="1"><span class="kw">confint</span>(fit.bar.nlme.bt, <span class="dt">type =</span> <span class="st">"perc"</span>)</a></code></pre></div>
<pre><code>## Bootstrap percent confidence intervals
##
## 2.5 % 97.5 %
## 1 306.707 382.6985</code></pre>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb48-1" data-line-number="1"><span class="kw">hist</span>(fit.bar.nlme.bt, <span class="dt">ci =</span> <span class="st">"perc"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>For this model the estimate for the asymptote is 349, but from the model fit we do not get a direct confidence interval. Bootstrapping makes this easy (but it takes a bit of time) and it results in 307, 383.</p>
</div>
<div id="confidence-bands-for-generalized-nonlinear-models" class="section level2">
<h2>Confidence bands for generalized nonlinear models</h2>
<p>For this example I will use the Orange dataset. The goal here is to place confidence bands around the mean response function.</p>
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb49-1" data-line-number="1"><span class="kw">data</span>(Orange)</a>
<a class="sourceLine" id="cb49-2" data-line-number="2"></a>
<a class="sourceLine" id="cb49-3" data-line-number="3"><span class="co">## This fits a model which considers the fact that </span></a>
<a class="sourceLine" id="cb49-4" data-line-number="4"><span class="co">## the variance typically increases as the fitted</span></a>
<a class="sourceLine" id="cb49-5" data-line-number="5"><span class="co">## values increase</span></a>
<a class="sourceLine" id="cb49-6" data-line-number="6">fitg <-<span class="st"> </span><span class="kw">gnls</span>(circumference <span class="op">~</span><span class="st"> </span><span class="kw">SSlogis</span>(age, Asym, xmid, scal), </a>
<a class="sourceLine" id="cb49-7" data-line-number="7"> <span class="dt">data =</span> Orange, <span class="dt">weights =</span> <span class="kw">varPower</span>())</a>
<a class="sourceLine" id="cb49-8" data-line-number="8"></a>
<a class="sourceLine" id="cb49-9" data-line-number="9"><span class="co">## Here we use bootstrapping to investigate </span></a>
<a class="sourceLine" id="cb49-10" data-line-number="10"><span class="co">## the uncertainty around the fitted values</span></a>
<a class="sourceLine" id="cb49-11" data-line-number="11">fitg.bt1 <-<span class="st"> </span><span class="kw">boot_nlme</span>(fitg, fitted, <span class="dt">psim =</span> <span class="dv">1</span>, <span class="dt">R =</span> <span class="dv">300</span>)</a></code></pre></div>
<pre><code>## Number of times model fit did not converge 3 out of 300</code></pre>
<div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb51-1" data-line-number="1"><span class="co">## Compute 90% quantile confidence bands</span></a>
<a class="sourceLine" id="cb51-2" data-line-number="2">lwr1.q <-<span class="st"> </span><span class="kw">apply</span>(<span class="kw">t</span>(fitg.bt1<span class="op">$</span>t), <span class="dv">1</span>, quantile, <span class="dt">probs =</span> <span class="fl">0.05</span>, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb51-3" data-line-number="3">upr1.q <-<span class="st"> </span><span class="kw">apply</span>(<span class="kw">t</span>(fitg.bt1<span class="op">$</span>t), <span class="dv">1</span>, quantile, <span class="dt">probs =</span> <span class="fl">0.95</span>, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb51-4" data-line-number="4"></a>
<a class="sourceLine" id="cb51-5" data-line-number="5"><span class="kw">ggplot</span>() <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb51-6" data-line-number="6"><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">data =</span> Orange, <span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> circumference)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb51-7" data-line-number="7"><span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">data =</span> Orange, <span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> <span class="kw">fitted</span>(fitg))) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb51-8" data-line-number="8"><span class="st"> </span><span class="kw">geom_ribbon</span>(<span class="kw">aes</span>(<span class="dt">x =</span> Orange<span class="op">$</span>age, <span class="dt">ymin =</span> lwr1.q, <span class="dt">ymax =</span> upr1.q), </a>
<a class="sourceLine" id="cb51-9" data-line-number="9"> <span class="dt">fill =</span> <span class="st">"purple"</span>, <span class="dt">alpha =</span> <span class="fl">0.2</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb51-10" data-line-number="10"><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Orange fit using the logistic: </span><span class="ch">\n</span><span class="st"> 90% confidence band for the mean function"</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="simulation" class="section level1">
<h1>Simulation</h1>
<p>Here I will continue with the Orange dataset and illustrate different types of simulation which are relevant depending on what the inference scope is. This simulation is, in fact, used inside of the bootstrap function above; it is a necessary step for bootstrap for these type of models.</p>
<div id="fitted-values" class="section level2">
<h2>Fitted values</h2>
<p>The first level of predictions we can generate for the Orange dataset are at the ‘population’ level. These are similar to the fitted values for the ‘gnls’ case above. Notice that we do not have the option of requesting standard errors for the predicitons for this model either.</p>
<div class="sourceCode" id="cb52"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb52-1" data-line-number="1">fmoL <-<span class="st"> </span><span class="kw">nlsList</span>(circumference <span class="op">~</span><span class="st"> </span><span class="kw">SSlogis</span>(age, Asym, xmid, scal), <span class="dt">data =</span> Orange)</a>
<a class="sourceLine" id="cb52-2" data-line-number="2"></a>
<a class="sourceLine" id="cb52-3" data-line-number="3">fmo <-<span class="st"> </span><span class="kw">nlme</span>(fmoL, <span class="dt">random =</span> <span class="kw">pdDiag</span>(Asym <span class="op">+</span><span class="st"> </span>xmid <span class="op">+</span><span class="st"> </span>scal <span class="op">~</span><span class="st"> </span><span class="dv">1</span>))</a>
<a class="sourceLine" id="cb52-4" data-line-number="4"></a>
<a class="sourceLine" id="cb52-5" data-line-number="5"><span class="co">## Just one simulation, because with psim = 0 and level = 0, we are </span></a>
<a class="sourceLine" id="cb52-6" data-line-number="6"><span class="co">## computing the predicted values at level = 0 (?predict.nlme)</span></a>
<a class="sourceLine" id="cb52-7" data-line-number="7">sim00 <-<span class="st"> </span><span class="kw">simulate_nlme</span>(fmo, <span class="dt">nsim =</span> <span class="dv">1</span>, <span class="dt">psim =</span> <span class="dv">0</span>, <span class="dt">level =</span> <span class="dv">0</span>)</a>
<a class="sourceLine" id="cb52-8" data-line-number="8"></a>
<a class="sourceLine" id="cb52-9" data-line-number="9">dat00 <-<span class="st"> </span><span class="kw">cbind</span>(Orange, <span class="dt">prd =</span> <span class="kw">as.vector</span>(sim00))</a>
<a class="sourceLine" id="cb52-10" data-line-number="10"></a>
<a class="sourceLine" id="cb52-11" data-line-number="11"><span class="kw">ggplot</span>(<span class="dt">data =</span> dat00) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb52-12" data-line-number="12"><span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> circumference)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb52-13" data-line-number="13"><span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> prd)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb52-14" data-line-number="14"><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"psim = 0, level = 0"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>One approach we can use to assess the uncertainty in the response would be to sample from the vector of parameters and the associated variance-covariance matrix. In this case, we could do many realizations, but I’ll keep it to just 100. A frequentist interpretation of the bands below would be that in repeated sampling from this same population we would expect that the true relationship will be within this band with a specified probability. One way to compute these bands (which I’m not doing below), would be to calculate the quantiles of the empirical distribution. These could be called confidence bands, which would be equivalent to the ‘interval = confidence’ in a linear model (?predict.lm).</p>
<div class="sourceCode" id="cb53"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb53-1" data-line-number="1">sdat10 <-<span class="st"> </span><span class="kw">simulate_nlme</span>(fmo, <span class="dt">nsim =</span> <span class="dv">100</span>, <span class="dt">psim =</span> <span class="dv">1</span>, <span class="dt">level =</span> <span class="dv">0</span>, <span class="dt">value =</span> <span class="st">"data.frame"</span>)</a>
<a class="sourceLine" id="cb53-2" data-line-number="2"></a>
<a class="sourceLine" id="cb53-3" data-line-number="3"><span class="kw">ggplot</span>(<span class="dt">data =</span> sdat10) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb53-4" data-line-number="4"><span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> y.sim, <span class="dt">group =</span> ii), <span class="dt">color =</span> <span class="st">"gray"</span>, <span class="dt">alpha =</span> <span class="fl">0.5</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb53-5" data-line-number="5"><span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> circumference)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb53-6" data-line-number="6"><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"psim = 1, level = 0"</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb53-7" data-line-number="7"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"circumference"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>In this case, each tree is an ‘individual’ and the previous one is a population level confidence. Next, we would like to produce individual level ‘prediction’ bands. This is, if we were able to resample from a population with a similar structure and if we wanted to have bands that include the true relationship between circumference and age for these trees, we could produce the confidence bands from the quantiles of the empirical distribution of the lines in the figure below (we would need a few thousand samples in order to do this).</p>
<div class="sourceCode" id="cb54"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb54-1" data-line-number="1">sdat11 <-<span class="st"> </span><span class="kw">simulate_nlme</span>(fmo, <span class="dt">nsim =</span> <span class="dv">100</span>, <span class="dt">psim =</span> <span class="dv">1</span>, <span class="dt">level =</span> <span class="dv">1</span>, <span class="dt">value =</span> <span class="st">"data.frame"</span>)</a>
<a class="sourceLine" id="cb54-2" data-line-number="2"></a>
<a class="sourceLine" id="cb54-3" data-line-number="3"><span class="co">## We need a tree simulation ID</span></a>
<a class="sourceLine" id="cb54-4" data-line-number="4"><span class="co">## for plotting</span></a>
<a class="sourceLine" id="cb54-5" data-line-number="5">sdat11<span class="op">$</span>Tree_ID <-<span class="st"> </span><span class="kw">with</span>(sdat11, <span class="kw">paste0</span>(Tree,<span class="st">"_"</span>,ii))</a>
<a class="sourceLine" id="cb54-6" data-line-number="6"></a>
<a class="sourceLine" id="cb54-7" data-line-number="7"><span class="kw">ggplot</span>(<span class="dt">data =</span> sdat11) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb54-8" data-line-number="8"><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>Tree) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb54-9" data-line-number="9"><span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> y.sim, <span class="dt">color =</span> Tree, <span class="dt">group =</span> Tree_ID), </a>
<a class="sourceLine" id="cb54-10" data-line-number="10"> <span class="dt">alpha =</span> <span class="fl">0.5</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb54-11" data-line-number="11"><span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> circumference)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb54-12" data-line-number="12"><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"psim = 1, level = 1"</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb54-13" data-line-number="13"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"circumference"</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb54-14" data-line-number="14"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>Finally, the next level would be to produce intervals that would likely contain a future observation instead of just the relationship, which was captured in the figure above. The ‘bands’ below are wider than in the previous figure. The reason why the difference is small, is, because in this case the residual variance is comparatively small.</p>
<div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb55-1" data-line-number="1">sdat21 <-<span class="st"> </span><span class="kw">simulate_nlme</span>(fmo, <span class="dt">nsim =</span> <span class="dv">100</span>, <span class="dt">psim =</span> <span class="dv">2</span>, <span class="dt">level =</span> <span class="dv">1</span>, <span class="dt">value =</span> <span class="st">"data.frame"</span>)</a>
<a class="sourceLine" id="cb55-2" data-line-number="2"></a>
<a class="sourceLine" id="cb55-3" data-line-number="3"><span class="co">## Here I'm plotting points to emphasize that we are making</span></a>
<a class="sourceLine" id="cb55-4" data-line-number="4"><span class="co">## predictions at the level of a single observation</span></a>
<a class="sourceLine" id="cb55-5" data-line-number="5"><span class="kw">ggplot</span>(<span class="dt">data =</span> sdat21) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb55-6" data-line-number="6"><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>Tree) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb55-7" data-line-number="7"><span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> y.sim, <span class="dt">color =</span> Tree), </a>
<a class="sourceLine" id="cb55-8" data-line-number="8"> <span class="dt">alpha =</span> <span class="fl">0.5</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb55-9" data-line-number="9"><span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">x =</span> age, <span class="dt">y =</span> circumference)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb55-10" data-line-number="10"><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"psim = 2, level = 1"</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb55-11" data-line-number="11"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"circumference"</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb55-12" data-line-number="12"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>The previous examples are simulations, but in order to generate confidence intervals or bands we would need to perform bootstrap, but it takes much longer, so I’m not including it in this vignette.</p>
</div>
</div>
<div id="appendix" class="section level1">
<h1>Appendix</h1>
<div id="note-on-simulate-methodology" class="section level2">
<h2>Note on simulate methodology</h2>
<p>In the case of simulating data from a nonlinear model and how this relates to bootstrapping. I’ve been thinking about a number of things. In a nonlinear model, the mean function should account for a <em>substantial</em> part of the variability. If we think about models in terms of</p>
<p><span class="math display">\[
data = signal + noise
\]</span> And for mixed models: <span class="math display">\[
data = fixed + random + residual
\]</span></p>
<p>I can see how in the linear case often the random part can be of substantial interest and sometimes more important than the fixed part, but for nonlinear models, the <em>fixed</em> part should dominate, otherwise we would need a better function or the data are not amenable to nonlinear modeling. In simulate_nlme, psim = 0, gives you the fitted values, so should be equivalent to ‘predict.nlme’, if theres is ever a discrepancy, then that is a bug. For psim = 1, I sample from the vector of parameters assuming that they are normally distributed. This seems to be very reasonable in practice and, in fact, much more important is the variance-covariance matrix of the vector of parameters and in particular the correlations. The problem is that in nonlinear mixed models, the relationship among parameters is not linear and can look a bit like a ‘banana’. This can be investigated through the use of bootstrap. For psim = 2, I add the residual variability which considers the modeling of the variance (heterogeneous variances), but not the possible lack of independence of the residuals. (This seems to be unlikely to arise for the type of models that we usually fit. Adding the correlation structure of the residuals can be done, but I’ll work on it when the need arises - not a priority.) It would be possible to add an options for psim = 3, where I also consider the uncertainty in the ‘random’ part above, this is is better captures, in my opinion, the ‘level’ argument in the ‘simulate_nlme’ function. By default, ‘predict.nlme’ predicts at the deepest level in the hierarchy (i.e. Q) and changing this argument allows for simulations at the different levels. Another reason, for not implementing psim = 3 (meaning sampling for the random part) is that, often, there is meaning or interest in these specific random terms. For example, sites/locations/subjects/experimenta.units are higher or lower because, in part, some characteristics that are know and some unknown, but which are out of our control. For this reason, assigning resampling or assigning a deviation at random at this level might be undesirable. If we have 10 sites labeled “A” through “J” and we know that site “A” is higher due to some characteristics (which we do not control), it would not make sense to assign the deviation from site “A” to “J” (which might be the lowest). In our analysis it might still make sense to treat ‘sites’ as random. In this case, when we use simulate_nlme, it makes more sense to use ‘psim = 2, level = 1’ (‘site’ would be our deepest level in the hierarchy - like ‘Tree’ in the ‘Orange’ example above) to generate data which appears similar to the data which was actually collected. It is true that this does not allow us to incorporate the uncertainty from the fact that we obtained these specific random deviates at the level of site (or Tree), but these will vary anyway because we are simulating forward from the vector of fixed parameters, which should account for the majority of the variability in these models. For this reason, I know that my approach is not perfect, but, so far, it seems to be usable. I’m thinking about Morris argument (see References) and I tested whether there is bias in the estimate of the random term variances using my method and I did not detect a bias - but I’m not resampling from the BLUPs. I’m also thinking that the non-parameteric part of my method (resampling the residuals) is somewhat trivial and implementing a fully parameteric option is feasible.</p>
</div>
</div>
<div id="references" class="section level1">
<h1>References</h1>
<ul>
<li><p>J. Fox and S. Weisberg. An R Companion to Applied Regression. Sage, Thousand Oaks CA, 2nd edition, 2011. URL <a href="http://z.umn.edu/carbook" class="uri">http://z.umn.edu/carbook</a>.</p></li>
<li><p>J. Fox and S. Weisberg. Bootstrapping regression models in R. Technical report, 2017. URL: <a href="https://socialsciences.mcmaster.ca/jfox/Books/Companion-2E/appendix/Appendix-Bootstrapping.pdf" class="uri">https://socialsciences.mcmaster.ca/jfox/Books/Companion-2E/appendix/Appendix-Bootstrapping.pdf</a></p></li>
<li><p>J. Fox and S. Weisberg. Nonlinear Regression, Nonlinear Least Squares, and Nonlinear Mixed Models in R. Appendix, 2018. URL: <a href="https://socialsciences.mcmaster.ca/jfox/Books/Companion/appendices/Appendix-Nonlinear-Regression.pdf" class="uri">https://socialsciences.mcmaster.ca/jfox/Books/Companion/appendices/Appendix-Nonlinear-Regression.pdf</a></p></li>
<li><p>Morris, J. S. (2002). The BLUPs Are Not ‘best’ When It Comes to Bootstrapping. Statistics & Probability Letters 56(4): 425–430. <a href="https://doi.org/10.1016/S0167-7152(02)00041-X" class="uri">https://doi.org/10.1016/S0167-7152(02)00041-X</a>.</p></li>
<li><p><a href="http://sia.webpopix.org/nonlinearRegression.html#confidence-intervals-and-prediction-intervals" class="uri">http://sia.webpopix.org/nonlinearRegression.html#confidence-intervals-and-prediction-intervals</a></p></li>
<li><p>propagate: <a href="https://rmazing.wordpress.com/2013/08/31/introducing-propagate/" class="uri">https://rmazing.wordpress.com/2013/08/31/introducing-propagate/</a></p></li>
<li><p>For linear mixed models: package ‘lmeresampler’: <a href="https://CRAN.R-project.org/package=lmeresampler" class="uri">https://CRAN.R-project.org/package=lmeresampler</a></p></li>
<li><p><a href="https://stats.stackexchange.com/questions/231074/confidence-intervals-on-predictions-for-a-non-linear-mixed-model-nlme" class="uri">https://stats.stackexchange.com/questions/231074/confidence-intervals-on-predictions-for-a-non-linear-mixed-model-nlme</a></p></li>
<li><p><a href="https://rpubs.com/bbolker/3423" class="uri">https://rpubs.com/bbolker/3423</a></p></li>
</ul>
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