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<title>Empirical Null with Gaussian Derivatives: Numerical Issues</title>

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<h1 class="title toc-ignore">Empirical Null with Gaussian Derivatives: Numerical Issues</h1>
<h4 class="author"><em>Lei Sun</em></h4>
<h4 class="date"><em>2017-03-26</em></h4>

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<p><strong>Last updated:</strong> 2018-05-12</p>
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<hr />
<div id="introduction-instability-and-overfitting-k-can-be-too-large" class="section level2">
<h2>Introduction: instability and overfitting – <span class="math inline">\(K\)</span> can be too large</h2>
<p>Things can go wrong in choosing the optimal <span class="math inline">\(\hat K\)</span>. We are using <a href="gaussian_derivatives_2.html">an automated rule</a>, yet sometimes the objective (log-likelihood) could fail to pass the optimal criterion before the optimization becomes unstable.</p>
<p>Recall that we make <a href="gaussian_derivatives.html">two key assumptions</a> to make the problem tractable. <em>In place of the original second constraint of non-negativity, we use <span class="math inline">\(n\)</span> observed <span class="math inline">\(z\)</span> scores instead of all <span class="math inline">\(x\in\mathbb{R}\)</span>.</em> Therefore, when <span class="math inline">\(K\)</span> gets larger, and the higher order Gaussian derivatives involved get more complicated, it’s possible that the optimal solution will satisfy the non-negativity constraint for all <span class="math inline">\(n\)</span> observed <span class="math inline">\(z\)</span> scores, <strong>but not the whole real line.</strong> <em>This issue also happens to <a href="gaussian_derivatives_2.html">some well-behaved examples</a> if looked closely.</em></p>
<p>Meanwhile, sometimes an optimal <span class="math inline">\(\hat K\)</span> can be found according to <a href="gaussian_derivatives_2.html">the rule</a>, but it looks like overfitting. A <span class="math inline">\(K &lt; \hat K\)</span> appears better.</p>
<p>Here we have two examples.</p>
<pre class="r"><code>source(&quot;../code/ecdfz.R&quot;)</code></pre>
<pre class="r"><code>z = read.table(&quot;../output/z_null_liver_777.txt&quot;)
p = read.table(&quot;../output/p_null_liver_777.txt&quot;)</code></pre>
<pre class="r"><code>library(ashr)
DataSet = c(522, 724)
res_DataSet = list()
for (i in 1:length(DataSet)) {
  zscore = as.numeric(z[DataSet[i], ])
  fit.ecdfz = ecdfz.optimal(zscore)
  fit.ash = ash(zscore, 1, method = &quot;fdr&quot;)
  fit.ash.pi0 = get_pi0(fit.ash)
  pvalue = as.numeric(p[DataSet[i], ])
  fd.bh = sum(p.adjust(pvalue, method = &quot;BH&quot;) &lt;= 0.05)
  res_DataSet[[i]] = list(DataSet = DataSet[i], fit.ecdfz = fit.ecdfz, fit.ash = fit.ash, fit.ash.pi0 = fit.ash.pi0, fd.bh = fd.bh, zscore = zscore, pvalue = pvalue)
}</code></pre>
<pre class="r"><code>library(EQL)
x.pt = seq(-5, 5, 0.01)
H.pt = sapply(1:15, EQL::hermite, x = x.pt)</code></pre>
</div>
<div id="example-1-numerical-instability-when-k-is-too-large" class="section level2">
<h2>Example 1: Numerical instability when <span class="math inline">\(K\)</span> is too large</h2>
<pre><code>Data Set 724 : Number of BH&#39;s False Discoveries: 79 ; ASH&#39;s pihat0 = 0.01606004</code></pre>
<p><img src="figure/gaussian_derivatives_3.rmd/unnamed-chunk-5-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/gaussian_derivatives_3.rmd/unnamed-chunk-5-2.png" width="672" style="display: block; margin: auto;" /></p>
<p>In this example <a href="gaussian_derivatives_2.html">the automated rule</a> fails to find an optimal <span class="math inline">\(\hat K\)</span>. Note that the fitted log-likelihood increased until seemingly reached a plateau, but didn’t quite make the cut. After that, as <span class="math inline">\(K\)</span> keeps getting larger, the optimization becomes unstable. The blue <span class="math inline">\(K = 14\)</span> line obviously breaks <a href="gaussian_derivatives.html">the non-negativity constraint</a> for <span class="math inline">\(x \neq z_i\)</span>, the <span class="math inline">\(n\)</span> observed <span class="math inline">\(z\)</span> scores.</p>
</div>
<div id="example-2-overfitting-when-k-is-larger-than-what-appears-necessary" class="section level2">
<h2>Example 2: Overfitting when <span class="math inline">\(K\)</span> is larger than what appears necessary</h2>
<pre><code>Data Set 522 : Number of BH&#39;s False Discoveries: 4 ; ASH&#39;s pihat0 = 0.02083846</code></pre>
<p><img src="figure/gaussian_derivatives_3.rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/gaussian_derivatives_3.rmd/unnamed-chunk-6-2.png" width="672" style="display: block; margin: auto;" /></p>
<p>In this example <a href="gaussian_derivatives_2.html">the automated rule</a> is able to find an optimal <span class="math inline">\(\hat K = 8\)</span>. However, the green <span class="math inline">\(K = 6\)</span> lines seems better visually. Their difference in the fitted log-likelihood is very small, although larger than what the rule requires.</p>
</div>
<div id="conclusion" class="section level2">
<h2>Conclusion</h2>
<p>Things can go very wrong when the number of fitted Gaussian derivatives <span class="math inline">\(K\)</span> is too large, and it implies that we cannot blindly fit ever growing <span class="math inline">\(K\)</span> and hope the fitted log-likelihood converges. On the other hand, the good news is oftentimes we can still reach a pattern of increasing log-likelihoods, which gives a reasonable <span class="math inline">\(K\)</span>, before the optimization becomes unstable, although it might be not the optimal <span class="math inline">\(K\)</span> we would find by the current log-likelihood ratio test motivated rule.</p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.4

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] CVXR_0.95     EQL_1.0-0     ttutils_1.0-1

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.16      knitr_1.20        whisker_0.3-2    
 [4] magrittr_1.5      workflowr_1.0.1   bit_1.1-12       
 [7] lattice_0.20-35   R6_2.2.2          stringr_1.3.0    
[10] tools_3.4.3       grid_3.4.3        R.oo_1.21.0      
[13] git2r_0.21.0      scs_1.1-1         htmltools_0.3.6  
[16] bit64_0.9-7       yaml_2.1.18       rprojroot_1.3-2  
[19] digest_0.6.15     gmp_0.5-13.1      Matrix_1.2-12    
[22] ECOSolveR_0.4     R.utils_2.6.0     evaluate_0.10.1  
[25] rmarkdown_1.9     stringi_1.1.6     Rmpfr_0.6-1      
[28] compiler_3.4.3    backports_1.1.2   R.methodsS3_1.7.1</code></pre>
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