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README.html
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README.html
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<!DOCTYPE html>
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<!-- README.md is generated from README.Rmd. Please edit that file -->
<h1 id="rhaldensify">R/<code>haldensify</code></h1>
<p><a href="https://travis-ci.org/nhejazi/haldensify"><img src="data:image/svg+xml;base64,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" alt="Travis-CI Build Status" /></a> <a href="https://ci.appveyor.com/project/nhejazi/haldensify"><img src="data:image/svg+xml;base64,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" alt="AppVeyor Build Status" /></a> <a href="https://codecov.io/github/nhejazi/haldensify?branch=master"><img src="data:image/svg+xml;charset=utf-8;base64,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" alt="Coverage Status" /></a> <a href="http://www.r-pkg.org/pkg/haldensify"><img src="data:image/svg+xml; 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<blockquote>
<p>Nonparametric Conditional Density Estimation with the Highly Adaptive Lasso</p>
</blockquote>
<p><strong>Author:</strong> <a href="https://nimahejazi.org">Nima Hejazi</a></p>
<hr />
<h2 id="whats-haldensify">What’s <code>haldensify</code>?</h2>
<p>The <code>haldensify</code> R package is designed to provide facilities for nonparametric conditional density estimation based on the procedure proposed by Díaz and van der Laan (2011). The core of the implemented methodology involves recovering conditional density estimates by performing pooled hazards regressions so as to assess the conditional hazard that an observation falls in a given bin over the support of the variable of interest. Such conditional density estimates are required to estimate the propensity score when the intervention variable considered is continuous (Díaz and van der Laan 2012, 2018; Díaz and Hejazi 2019). Though future generalization of the core routines may be possible, for the time being, <code>haldensify</code> is a minimal implementation of this strategy for use only with the highly adaptive lasso (Benkeser and van der Laan 2016; van der Laan 2017; van der Laan and Benkeser 2018; Coyle and Hejazi 2018).</p>
<hr />
<h2 id="installation">Installation</h2>
<p>Install the most recent <em>stable release</em> from GitHub via <a href="https://www.rstudio.com/products/rpackages/devtools/"><code>devtools</code></a>:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1">devtools<span class="op">::</span><span class="kw">install_github</span>(<span class="st">"nhejazi/haldensify"</span>)</a></code></pre></div>
<hr />
<h2 id="example">Example</h2>
<p>A simple example illustrates how <code>haldensify</code> may be used to construct conditional density estimates:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw">library</span>(data.table)</a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw">library</span>(tidyverse)</a>
<a class="sourceLine" id="cb2-3" data-line-number="3"><span class="kw">library</span>(hal9001)</a>
<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="kw">library</span>(haldensify)</a>
<a class="sourceLine" id="cb2-5" data-line-number="5"><span class="kw">set.seed</span>(<span class="dv">76924</span>)</a>
<a class="sourceLine" id="cb2-6" data-line-number="6"></a>
<a class="sourceLine" id="cb2-7" data-line-number="7"><span class="co"># simulate data: W ~ Rademacher and A|W ~ N(mu = \pm 1, sd = 0.5)</span></a>
<a class="sourceLine" id="cb2-8" data-line-number="8">n_train <-<span class="st"> </span><span class="dv">1000</span></a>
<a class="sourceLine" id="cb2-9" data-line-number="9">w <-<span class="st"> </span><span class="kw">rbinom</span>(n_train, <span class="dv">1</span>, <span class="fl">0.5</span>)</a>
<a class="sourceLine" id="cb2-10" data-line-number="10">w[w <span class="op">==</span><span class="st"> </span><span class="dv">0</span>] <-<span class="st"> </span><span class="dv">-1</span></a>
<a class="sourceLine" id="cb2-11" data-line-number="11">a <-<span class="st"> </span><span class="kw">rnorm</span>(n_train, w, <span class="fl">0.5</span>)</a>
<a class="sourceLine" id="cb2-12" data-line-number="12"></a>
<a class="sourceLine" id="cb2-13" data-line-number="13"><span class="co"># learn relationship A|W using HAL-based density estimation procedure</span></a>
<a class="sourceLine" id="cb2-14" data-line-number="14">mod_haldensify <-<span class="st"> </span><span class="kw">haldensify</span>(</a>
<a class="sourceLine" id="cb2-15" data-line-number="15"> <span class="dt">A =</span> a, <span class="dt">W =</span> w,</a>
<a class="sourceLine" id="cb2-16" data-line-number="16"> <span class="dt">grid_type =</span> <span class="st">"equal_range"</span>,</a>
<a class="sourceLine" id="cb2-17" data-line-number="17"> <span class="dt">n_bins =</span> <span class="dv">10</span>,</a>
<a class="sourceLine" id="cb2-18" data-line-number="18"> <span class="dt">lambda_seq =</span> <span class="kw">exp</span>(<span class="kw">seq</span>(<span class="op">-</span><span class="dv">1</span>, <span class="dv">-13</span>, <span class="dt">length =</span> <span class="dv">1000</span>))</a>
<a class="sourceLine" id="cb2-19" data-line-number="19">)</a>
<a class="sourceLine" id="cb2-20" data-line-number="20"></a>
<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># predictions to recover conditional density of A|W</span></a>
<a class="sourceLine" id="cb2-22" data-line-number="22">new_a <-<span class="st"> </span><span class="kw">seq</span>(<span class="op">-</span><span class="dv">2</span>, <span class="dv">2</span>, <span class="dt">by =</span> <span class="fl">0.01</span>)</a>
<a class="sourceLine" id="cb2-23" data-line-number="23">new_w_neg <-<span class="st"> </span><span class="kw">rep</span>(<span class="op">-</span><span class="dv">1</span>, <span class="kw">length</span>(new_a))</a>
<a class="sourceLine" id="cb2-24" data-line-number="24">new_w_pos <-<span class="st"> </span><span class="kw">rep</span>(<span class="dv">1</span>, <span class="kw">length</span>(new_a))</a>
<a class="sourceLine" id="cb2-25" data-line-number="25">new_dat <-<span class="st"> </span><span class="kw">as.data.table</span>(<span class="kw">list</span>(<span class="dt">a =</span> new_a, <span class="dt">w_neg =</span> new_w_neg, <span class="dt">w_pos =</span> new_w_pos))</a>
<a class="sourceLine" id="cb2-26" data-line-number="26">new_dat<span class="op">$</span>pred_w_neg <-<span class="st"> </span><span class="kw">predict</span>(mod_haldensify,</a>
<a class="sourceLine" id="cb2-27" data-line-number="27"> <span class="dt">new_A =</span> new_dat<span class="op">$</span>a, <span class="dt">new_W =</span> new_dat<span class="op">$</span>w_neg)</a>
<a class="sourceLine" id="cb2-28" data-line-number="28">new_dat<span class="op">$</span>pred_w_pos <-<span class="st"> </span><span class="kw">predict</span>(mod_haldensify,</a>
<a class="sourceLine" id="cb2-29" data-line-number="29"> <span class="dt">new_A =</span> new_dat<span class="op">$</span>a, <span class="dt">new_W =</span> new_dat<span class="op">$</span>w_pos)</a>
<a class="sourceLine" id="cb2-30" data-line-number="30">new_dat<span class="op">$</span>true_w_neg <-<span class="st"> </span><span class="kw">dnorm</span>(new_a, <span class="dt">mean =</span> <span class="kw">unique</span>(new_w_neg), <span class="dt">sd =</span> <span class="fl">0.5</span>)</a>
<a class="sourceLine" id="cb2-31" data-line-number="31">new_dat<span class="op">$</span>true_w_pos <-<span class="st"> </span><span class="kw">dnorm</span>(new_a, <span class="dt">mean =</span> <span class="kw">unique</span>(new_w_pos), <span class="dt">sd =</span> <span class="fl">0.5</span>)</a>
<a class="sourceLine" id="cb2-32" data-line-number="32"></a>
<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># visualize results</span></a>
<a class="sourceLine" id="cb2-34" data-line-number="34">p <-<span class="st"> </span>new_dat <span class="op">%>%</span></a>
<a class="sourceLine" id="cb2-35" data-line-number="35"><span class="st"> </span><span class="kw">melt</span>(<span class="dt">id =</span> <span class="kw">c</span>(<span class="st">"a"</span>), <span class="dt">measure.vars =</span> <span class="kw">c</span>(<span class="st">"pred_w_pos"</span>, <span class="st">"pred_w_neg"</span>,</a>
<a class="sourceLine" id="cb2-36" data-line-number="36"> <span class="st">"true_w_neg"</span>, <span class="st">"true_w_pos"</span>)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb2-37" data-line-number="37"><span class="st"> </span><span class="kw">mutate</span>(</a>
<a class="sourceLine" id="cb2-38" data-line-number="38"> <span class="dt">variable =</span> <span class="kw">str_remove</span>(variable, <span class="st">"pred_"</span>),</a>
<a class="sourceLine" id="cb2-39" data-line-number="39"> <span class="dt">variable =</span> <span class="kw">str_remove</span>(variable, <span class="st">"true_"</span>)</a>
<a class="sourceLine" id="cb2-40" data-line-number="40"> ) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb2-41" data-line-number="41"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> a, <span class="dt">y =</span> value, <span class="dt">color =</span> variable)) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-42" data-line-number="42"><span class="st"> </span><span class="kw">geom_line</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb2-43" data-line-number="43"><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Observed value"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-44" data-line-number="44"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Predicted probability"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-45" data-line-number="45"><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Conditional density p(A|W)"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-46" data-line-number="46"><span class="st"> </span><span class="kw">theme_bw</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb2-47" data-line-number="47"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</a>
<a class="sourceLine" id="cb2-48" data-line-number="48">p</a></code></pre></div>
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/><!-- --></p>
<hr />
<h2 id="issues">Issues</h2>
<p>If you encounter any bugs or have any specific feature requests, please <a href="https://github.com/nhejazi/haldensify/issues">file an issue</a>.</p>
<hr />
<h2 id="contributions">Contributions</h2>
<p>Contributions are very welcome. Interested contributors should consult our <a href="https://github.com/nhejazi/haldensify/blob/master/CONTRIBUTING.md">contribution guidelines</a> prior to submitting a pull request.</p>
<hr />
<h2 id="citation">Citation</h2>
<p>After using the <code>haldensify</code> R package, please cite the following:</p>
<pre><code> @manual{hejazi2019haldensify,
author = {Hejazi, Nima S},
title = {haldensify: Nonparametric conditional density estimation
with the highly adaptive lasso in {R}},
year = {2019},
url = {https://github.com/nhejazi/haldensify},
note = {R package version 0.0.1}
}
</code></pre>
<hr />
<h2 id="related">Related</h2>
<ul>
<li><a href="https://github.com/osofr/condensier">R/<code>condensier</code></a> - An independent implementation of the same core methodology, though more general in its making allowance for arbitrary selection of regression functions and a greater variety of hazard regression strategies.</li>
</ul>
<hr />
<h2 id="license">License</h2>
<p>© 2019 <a href="https://nimahejazi.org">Nima S. Hejazi</a></p>
<p>The contents of this repository are distributed under the MIT license. See below for details:</p>
<pre><code>MIT License
Copyright (c) 2019 Nima S. Hejazi
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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</code></pre>
<hr />
<h2 id="references">References</h2>
<div id="refs" class="references">
<div id="ref-benkeser2016highly">
<p>Benkeser, David, and Mark J van der Laan. 2016. “The Highly Adaptive Lasso Estimator.” In <em>Proceedings of the International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics</em>, 2016:689. NIH Public Access.</p>
</div>
<div id="ref-coyle2018hal9001">
<p>Coyle, Jeremy R, and Nima S Hejazi. 2018. <em>hal9001: The Scalable Highly Adaptive LASSO</em>. <a href="https://github.com/tlverse/hal9001" class="uri">https://github.com/tlverse/hal9001</a>.</p>
</div>
<div id="ref-diaz2019causal">
<p>Díaz, Iván, and Nima S Hejazi. 2019. “Causal Mediation Analysis for Stochastic Interventions.” <em>Submitted</em>. <a href="https://arxiv.org/abs/1901.02776" class="uri">https://arxiv.org/abs/1901.02776</a>.</p>
</div>
<div id="ref-diaz2011super">
<p>Díaz, Iván, and Mark J van der Laan. 2011. “Super Learner Based Conditional Density Estimation with Application to Marginal Structural Models.” <em>The International Journal of Biostatistics</em> 7 (1). De Gruyter: 1–20.</p>
</div>
<div id="ref-diaz2012population">
<p>———. 2012. “Population Intervention Causal Effects Based on Stochastic Interventions.” <em>Biometrics</em> 68 (2). Wiley Online Library: 541–49.</p>
</div>
<div id="ref-diaz2018stochastic">
<p>———. 2018. “Stochastic Treatment Regimes.” In <em>Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies</em>, 167–80. Springer Science & Business Media.</p>
</div>
<div id="ref-vdl2017generally">
<p>van der Laan, Mark J. 2017. “A Generally Efficient Targeted Minimum Loss Based Estimator Based on the Highly Adaptive Lasso.” <em>The International Journal of Biostatistics</em> 13 (2). De Gruyter.</p>
</div>
<div id="ref-vdl2018highly">
<p>van der Laan, Mark J, and David Benkeser. 2018. “Highly Adaptive Lasso (HAL).” In <em>Targeted Learning in Data Science</em>, 77–94. Springer.</p>
</div>
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