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<h1 class="title toc-ignore">Mash Missing Whole Row</h1>
<h4 class="author"><em>Yuxin Zou</em></h4>
<h4 class="date"><em>2018-2-28</em></h4>
</div>
<!-- Update knitr chunk options -->
<!-- Insert the date the file was last updated -->
<p><strong>Last updated:</strong> 2018-03-06</p>
<!-- Insert the code version (Git commit SHA1) if Git repository exists and R
package git2r is installed -->
<p><strong>Code version:</strong> 193208d</p>
<div id="r5" class="section level1">
<h1>R=5</h1>
<pre class="r"><code>library(mashr)</code></pre>
<pre><code>Loading required package: ashr</code></pre>
<pre class="r"><code>set.seed(2018)
data = simple_sims(500, err_sd = 0.1)
data.miss = data
# select missing rows
missing.row = sample(1:nrow(data$Bhat), nrow(data$Bhat)/4)
missing.ind = matrix(0, nrow = nrow(data$B), ncol=ncol(data$B))
missing.ind[missing.row,] = 1
# set missing rows to NA
missing = which(missing.ind == 1)
data.miss$Bhat[missing] = NA
data.miss$Shat[missing] = NA
missing1 = is.na(data.miss$Bhat[,1])
# set the missing value with large standard deviation
data.large.dev = data.miss
data.large.dev$Bhat[is.na(data.miss$Bhat)] = 0
data.large.dev$Shat[is.na(data.miss$Shat)] = 1000</code></pre>
<div id="ee-model" class="section level2">
<h2>EE model</h2>
<pre class="r"><code># with missing values
mash.data.missing = mash_set_data(Bhat=data.large.dev$Bhat, Shat=data.large.dev$Shat)
U.c = cov_canonical(mash.data.missing)
mash.model.missing = mash(mash.data.missing, U.c, verbose = FALSE)
# delete missing values
data.miss.na = data.miss
data.miss.na$Bhat = na.omit(data.miss$Bhat)
data.miss.na$Shat = na.omit(data.miss$Shat)
mash.data.missing.na = mash_set_data(Bhat=data.miss.na$Bhat, Shat= data.miss.na$Shat)
U.c = cov_canonical(mash.data.missing.na)
mash.model.missing.na = mash(mash.data.missing.na, U.c, verbose=FALSE)</code></pre>
<pre><code>Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
consider reducing rtol</code></pre>
<pre><code>Warning in mixIP(matrix_lik = structure(c(1, 0.204132576568413,
0.736530341806702, : Optimization step yields mixture weights that are
either too small, or negative; weights have been corrected and renormalized
after the optimization.</code></pre>
<pre class="r"><code>par(mfrow=c(1,2))
barplot(get_estimated_pi(mash.model.missing), las=2, cex.names = 0.7, main='with missing values')
barplot(get_estimated_pi(mash.model.missing.na), las=2, cex.names = 0.7, main = 'delete missing values')</code></pre>
<p><img src="figure/mash_missing_whole_row.Rmd/unnamed-chunk-4-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>par(mfrow=c(1,1))</code></pre>
</div>
<div id="ez-model" class="section level2">
<h2>EZ model</h2>
<pre class="r"><code># with missing values
mash.data.missing.1 = mash_set_data(Bhat=data.large.dev$Bhat, Shat=data.large.dev$Shat, alpha = 1)
U.c = cov_canonical(mash.data.missing.1)
mash.model.missing.1 = mash(mash.data.missing.1, U.c, verbose = FALSE)</code></pre>
<pre><code>FIXME: 'compute_posterior_matrices' in Rcpp does not transfer EZ to EE</code></pre>
<pre class="r"><code># delete missing values
mash.data.missing.na.1 = mash_set_data(Bhat=data.miss.na$Bhat, Shat= data.miss.na$Shat, alpha = 1)
U.c = cov_canonical(mash.data.missing.na.1)
mash.model.missing.na.1 = mash(mash.data.missing.na.1, U.c, verbose=FALSE)</code></pre>
<pre><code>Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
consider reducing rtol</code></pre>
<pre><code>Warning in mixIP(matrix_lik = structure(c(1, 0.204132576568412,
0.736530341806701, : Optimization step yields mixture weights that are
either too small, or negative; weights have been corrected and renormalized
after the optimization.</code></pre>
<pre><code>FIXME: 'compute_posterior_matrices' in Rcpp does not transfer EZ to EE</code></pre>
<pre class="r"><code>par(mfrow=c(1,2))
barplot(get_estimated_pi(mash.model.missing.1), las=2, cex.names = 0.7, main='with missing values')
barplot(get_estimated_pi(mash.model.missing.na.1), las=2, cex.names = 0.7, main='delete missing values')</code></pre>
<p><img src="figure/mash_missing_whole_row.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>par(mfrow=c(1,1))</code></pre>
</div>
</div>
<div id="r60" class="section level1">
<h1>R=60</h1>
<pre class="r"><code>set.seed(2018)
data = simple_sims(500, ncond = 60, err_sd = 0.1)
data.miss = data
missing.row = sample(1:nrow(data$Bhat), nrow(data$Bhat)/4)
missing.ind = matrix(0, nrow = nrow(data$B), ncol=ncol(data$B))
missing.ind[missing.row,] = 1
missing = which(missing.ind == 1)
data.miss$Bhat[missing] = NA
data.miss$Shat[missing] = NA
missing1 = is.na(data.miss$Bhat[,1])
# Set the missing value with large standard deviation
data.large.dev = data.miss
data.large.dev$Bhat[is.na(data.miss$Bhat)] = 0
data.large.dev$Shat[is.na(data.miss$Shat)] = 1000</code></pre>
<div id="ee-model-1" class="section level2">
<h2>EE model</h2>
<div id="with-missing-values" class="section level3">
<h3>With missing values</h3>
<pre class="r"><code>mash.data.missing = mash_set_data(Bhat=data.large.dev$Bhat, Shat=data.large.dev$Shat)
U.c = cov_canonical(mash.data.missing)
mash.model.missing = mash(mash.data.missing, U.c, verbose = FALSE)
barplot(get_estimated_pi(mash.model.missing), las=2, cex.names = 0.7)</code></pre>
<p><img src="figure/mash_missing_whole_row.Rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /> The weights learned from the data are correct.</p>
</div>
<div id="delete-missing-values" class="section level3">
<h3>Delete missing values</h3>
<pre class="r"><code>data.miss.na = data.miss
data.miss.na$Bhat = na.omit(data.miss$Bhat)
data.miss.na$Shat = na.omit(data.miss$Shat)
mash.data.missing.na = mash_set_data(Bhat=data.miss.na$Bhat, Shat= data.miss.na$Shat)
U.c = cov_canonical(mash.data.missing.na)
mash.model.missing.na = mash(mash.data.missing.na, U.c, verbose=FALSE)</code></pre>
<pre><code>Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
consider reducing rtol</code></pre>
<pre><code>Warning in mixIP(matrix_lik = structure(c(0.240920767436896,
0.337563330121546, : Optimization step yields mixture weights that are
either too small, or negative; weights have been corrected and renormalized
after the optimization.</code></pre>
<pre class="r"><code>barplot(get_estimated_pi(mash.model.missing.na), las=2, cex.names = 0.7)</code></pre>
<p><img src="figure/mash_missing_whole_row.Rmd/unnamed-chunk-9-1.png" width="672" style="display: block; margin: auto;" /> The estimated weights are very weird and incorrect. It ignores the null matrix.</p>
</div>
</div>
<div id="ez-model-1" class="section level2">
<h2>EZ model</h2>
<div id="with-missing-values-1" class="section level3">
<h3>With missing values</h3>
<pre class="r"><code>mash.data.missing.1 = mash_set_data(Bhat=data.large.dev$Bhat, Shat=data.large.dev$Shat, alpha = 1)
U.c = cov_canonical(mash.data.missing.1)
mash.model.missing.1 = mash(mash.data.missing.1, U.c, verbose = FALSE)</code></pre>
<pre><code>Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
consider reducing rtol</code></pre>
<pre><code>Warning in mixIP(matrix_lik = structure(c(0.240920767436896,
0.33756333012155, : Optimization step yields mixture weights that are
either too small, or negative; weights have been corrected and renormalized
after the optimization.</code></pre>
<pre><code>FIXME: 'compute_posterior_matrices' in Rcpp does not transfer EZ to EE</code></pre>
<pre class="r"><code>barplot(get_estimated_pi(mash.model.missing.1), las=2, cex.names = 0.7)</code></pre>
<p><img src="figure/mash_missing_whole_row.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="delete-missing-values-1" class="section level3">
<h3>Delete missing values</h3>
<pre class="r"><code>mash.data.missing.na.1 = mash_set_data(Bhat=data.miss.na$Bhat, Shat= data.miss.na$Shat, alpha = 1)
U.c = cov_canonical(mash.data.missing.na.1)
mash.model.missing.na.1 = mash(mash.data.missing.na.1, U.c, verbose=FALSE)</code></pre>
<pre><code>Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
consider reducing rtol</code></pre>
<pre><code>Warning in mixIP(matrix_lik = structure(c(0.240920767436896,
0.33756333012155, : Optimization step yields mixture weights that are
either too small, or negative; weights have been corrected and renormalized
after the optimization.</code></pre>
<pre><code>FIXME: 'compute_posterior_matrices' in Rcpp does not transfer EZ to EE</code></pre>
<pre class="r"><code>barplot(get_estimated_pi(mash.model.missing.na.1), las=2, cex.names = 0.7)</code></pre>
<p><img src="figure/mash_missing_whole_row.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /> The estimated weights are different.</p>
</div>
</div>
</div>
<div id="subgroups" class="section level1">
<h1>Subgroups</h1>
<p>The weired covariance structure is caused by the high dimension of R.</p>
<p>If we separate conditions into several groups, the covariance structure will be correct.</p>
<p>We show this for the EZ model R=60.</p>
<pre class="r"><code>mash.data.missing.1.sub1 = mash_set_data(Bhat=data.large.dev$Bhat[,1:20], Shat=data.large.dev$Shat[,1:20], alpha = 1)
U.c = cov_canonical(mash.data.missing.1.sub1)
mash.model.missing.1.sub1 = mash(mash.data.missing.1.sub1, U.c, verbose = FALSE)</code></pre>
<pre><code>FIXME: 'compute_posterior_matrices' in Rcpp does not transfer EZ to EE</code></pre>
<pre class="r"><code>mash.data.missing.1.sub2 = mash_set_data(Bhat=data.large.dev$Bhat[,21:40], Shat=data.large.dev$Shat[,21:40], alpha = 1)
U.c = cov_canonical(mash.data.missing.1.sub2)
mash.model.missing.1.sub2 = mash(mash.data.missing.1.sub2, U.c, verbose = FALSE)</code></pre>
<pre><code>FIXME: 'compute_posterior_matrices' in Rcpp does not transfer EZ to EE</code></pre>
<pre class="r"><code>mash.data.missing.1.sub3 = mash_set_data(Bhat=data.large.dev$Bhat[,41:60], Shat=data.large.dev$Shat[,41:60], alpha = 1)
U.c = cov_canonical(mash.data.missing.1.sub3)
mash.model.missing.1.sub3 = mash(mash.data.missing.1.sub3, U.c, verbose = FALSE)</code></pre>
<pre><code>FIXME: 'compute_posterior_matrices' in Rcpp does not transfer EZ to EE</code></pre>
<pre class="r"><code>par(mfcol=c(3,1))
barplot(get_estimated_pi(mash.model.missing.1.sub1), las=2, cex.names = 0.7, main='condition 1:20')
barplot(get_estimated_pi(mash.model.missing.1.sub2), las=2, cex.names = 0.7, main='condition 21:40')
barplot(get_estimated_pi(mash.model.missing.1.sub3), las=2, cex.names = 0.7,
main='condition 41:60')</code></pre>
<p><img src="figure/mash_missing_whole_row.Rmd/unnamed-chunk-12-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>par(mfcol=c(1,1))</code></pre>
</div>
<div id="session-information" class="section level1">
<h1>Session information</h1>
<!-- Insert the session information into the document -->
<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 10.13.3
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] mashr_0.2-6 ashr_2.2-7
loaded via a namespace (and not attached):
[1] Rcpp_0.12.15 knitr_1.20 magrittr_1.5
[4] REBayes_1.2 MASS_7.3-47 doParallel_1.0.11
[7] pscl_1.5.2 SQUAREM_2017.10-1 lattice_0.20-35
[10] foreach_1.4.4 plyr_1.8.4 stringr_1.3.0
[13] tools_3.4.3 parallel_3.4.3 grid_3.4.3
[16] rmeta_2.16 git2r_0.20.0 htmltools_0.3.6
[19] iterators_1.0.9 assertthat_0.2.0 yaml_2.1.17
[22] rprojroot_1.2 digest_0.6.13 Matrix_1.2-12
[25] codetools_0.2-15 evaluate_0.10.1 rmarkdown_1.8
[28] stringi_1.1.6 compiler_3.4.3 Rmosek_8.0.69
[31] backports_1.1.2 mvtnorm_1.0-7 truncnorm_1.0-8 </code></pre>
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