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<div id="researcher-degree-of-freedom-simulation" class="section level1">
<h1>Researcher degree of freedom simulation</h1>
<p><code>bsub -a openmpi -q mpi -W 48:00 -n 150,200 mpirun.lsf R --slave -f /usr/users/rarslan/relationship_dynamics/1_researcher_df.R</code> thanks to bbolker <a href="http://rpubs.com/bbolker/lme4sims" class="uri">http://rpubs.com/bbolker/lme4sims</a> & <a href="http://cran.r-project.org/web/packages/foreach/vignettes/nested.pdf" class="uri">http://cran.r-project.org/web/packages/foreach/vignettes/nested.pdf</a></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">options</span>(<span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>)
library =<span class="st"> </span>function (...) <span class="kw">suppressMessages</span>(base::<span class="kw">library</span>(...))
<span class="kw">library</span>(dplyr)</code></pre></div>
<p>set up MPI</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">setwd</span>(<span class="st">"/usr/users/rarslan/relationship_dynamics/"</span>)
<span class="kw">library</span>(doMPI)
cl <-<span class="st"> </span><span class="kw">startMPIcluster</span>(<span class="dt">verbose =</span> T)
<span class="kw">registerDoMPI</span>(cl)
<span class="kw">source</span>(<span class="st">"0_simulation_functions.R"</span>)</code></pre></div>
<div id="parameters" class="section level2">
<h2>Parameters</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sample_people =<span class="st"> </span><span class="kw">c</span>(<span class="dv">25</span>, <span class="dv">50</span>, <span class="dv">70</span>, <span class="dv">100</span>)
nr_of_days =<span class="st"> </span><span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">30</span>, <span class="dv">90</span>)
dayspans =<span class="st"> </span><span class="kw">list</span>(<span class="st">"1:38"</span> =<span class="st"> </span><span class="dv">1</span>:<span class="dv">38</span>, <span class="st">"18,5"</span> =<span class="st"> </span><span class="kw">c</span>(<span class="dv">5</span>, <span class="dv">18</span>))
fertility_effects =<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>)
trait_effects =<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.5</span>, <span class="dv">1</span>)
miss_windows =<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">2</span>)
researcher_degrees =<span class="st"> </span><span class="kw">list</span>(rdf_none, rdf_optional_stopping, rdf_covariate, rdf_outcome, rdf_predictor, rdf_outcome_covariate, rdf_predictor_outcome_covariate, rdf_stopping_predictor_outcome_covariate, rdf_stopping_predictor)
<span class="kw">names</span>(researcher_degrees) =<span class="st"> </span><span class="kw">list</span>(<span class="st">'rdf_none '</span>,<span class="st">'rdf_optional_stopping'</span>, <span class="st">'rdf_covariate'</span>, <span class="st">'rdf_outcome'</span>, <span class="st">'rdf_predictor'</span>, <span class="st">'rdf_outcome_covariate'</span>, <span class="st">'rdf_predictor_outcome_covariate'</span>, <span class="st">'rdf_stopping_predictor_outcome_covariate'</span>, <span class="st">'rdf_stopping_predictor'</span>)
nrep <-<span class="st"> </span><span class="dv">1</span>:<span class="dv">1000</span>
res0 <-<span class="st"> </span><span class="kw">rdf_stopping_predictor_outcome_covariate</span>(<span class="kw">simulate_fertility_effect</span>())</code></pre></div>
</div>
<div id="time-prediction-number-of-nodes" class="section level2">
<h2>Time prediction, number of nodes</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># time = system.time({ for (i in 1:10) { rdf_stopping_predictor_outcome_covariate(simulate_fertility_effect()) }})</span>
<span class="co"># length(sample_people) *</span>
<span class="co"># length(nr_of_days) *</span>
<span class="co"># length(dayspans) *</span>
<span class="co"># length(fertility_effects) *# nodes</span>
<span class="co"># length(trait_effects) *</span>
<span class="co"># length(miss_windows)</span>
<span class="co">#</span>
<span class="co"># length(researcher_degrees) * 10 * time[3] / 60 / 60 # hours per node</span></code></pre></div>
</div>
<div id="simulation" class="section level2">
<h2>Simulation</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">resdf =<span class="st"> </span><span class="kw">foreach</span>(<span class="dt">i =</span> <span class="kw">seq_along</span>(sample_people), <span class="dt">.combine =</span> <span class="st">"bind_rows"</span>) %:%
<span class="st"> </span><span class="kw">foreach</span>(<span class="dt">j =</span> <span class="kw">seq_along</span>(nr_of_days), <span class="dt">.combine =</span> <span class="st">"bind_rows"</span>) %:%
<span class="st"> </span><span class="kw">foreach</span>(<span class="dt">k =</span> <span class="kw">seq_along</span>(dayspans), <span class="dt">.combine =</span> <span class="st">"bind_rows"</span>) %:%
<span class="st"> </span><span class="kw">foreach</span>(<span class="dt">l =</span> <span class="kw">seq_along</span>(fertility_effects), <span class="dt">.combine =</span> <span class="st">"bind_rows"</span>) %:%
<span class="st"> </span><span class="kw">foreach</span>(<span class="dt">m =</span> <span class="kw">seq_along</span>(trait_effects), <span class="dt">.combine =</span> <span class="st">"bind_rows"</span>) %:%
<span class="st"> </span><span class="kw">foreach</span>(<span class="dt">n =</span> <span class="kw">seq_along</span>(miss_windows), <span class="dt">.combine =</span> <span class="st">"bind_rows"</span>, <span class="dt">.errorhandling =</span> <span class="st">"remove"</span>) %dopar%<span class="st"> </span>{
<span class="kw">source</span>(<span class="st">"0_simulation_functions.R"</span>)
<span class="co">#' ## Parameters</span>
sample_people =<span class="st"> </span><span class="kw">c</span>(<span class="dv">25</span>, <span class="dv">50</span>, <span class="dv">70</span>, <span class="dv">100</span>)
nr_of_days =<span class="st"> </span><span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">30</span>, <span class="dv">90</span>)
dayspans =<span class="st"> </span><span class="kw">list</span>(<span class="st">"1:38"</span> =<span class="st"> </span><span class="dv">1</span>:<span class="dv">38</span>, <span class="st">"18,5"</span> =<span class="st"> </span><span class="kw">c</span>(<span class="dv">5</span>, <span class="dv">18</span>))
fertility_effects =<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>)
trait_effects =<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.5</span>, <span class="dv">1</span>)
miss_windows =<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">2</span>)
researcher_degrees =<span class="st"> </span><span class="kw">list</span>(rdf_none, rdf_optional_stopping, rdf_covariate, rdf_outcome, rdf_predictor, rdf_outcome_covariate, rdf_predictor_outcome_covariate, rdf_stopping_predictor_outcome_covariate, rdf_stopping_predictor)
<span class="kw">names</span>(researcher_degrees) =<span class="st"> </span><span class="kw">list</span>(<span class="st">'rdf_none '</span>,<span class="st">'rdf_optional_stopping'</span>, <span class="st">'rdf_covariate'</span>, <span class="st">'rdf_outcome'</span>, <span class="st">'rdf_predictor'</span>, <span class="st">'rdf_outcome_covariate'</span>, <span class="st">'rdf_predictor_outcome_covariate'</span>, <span class="st">'rdf_stopping_predictor_outcome_covariate'</span>, <span class="st">'rdf_stopping_predictor'</span>)
nrep <-<span class="st"> </span><span class="dv">1</span>:<span class="dv">1000</span>
<span class="kw">foreach</span>(<span class="dt">repetition =</span> nrep, <span class="dt">.combine =</span> <span class="st">"bind_rows"</span>, <span class="dt">.maxcombine =</span> <span class="dv">1000</span>, <span class="dt">.errorhandling =</span> <span class="st">"remove"</span>) %do%<span class="st"> </span>{
<span class="kw">cat</span>(i, j, k, l, m, n, <span class="st">"</span><span class="ch">\n</span><span class="st">"</span>)
simulated_data =<span class="st"> </span><span class="kw">simulate_fertility_effect</span>(
<span class="dt">nr_of_people =</span> sample_people[i],
<span class="dt">nr_days =</span> nr_of_days[j],
<span class="dt">dayspan =</span> dayspans[[k]],
<span class="dt">miss_window =</span> miss_windows[n],
<span class="dt">effects =</span> <span class="kw">list</span>(
<span class="dt">trait =</span> trait_effects[m],
<span class="dt">fertility_effect =</span> fertility_effects[l],
<span class="dt">noise =</span> <span class="fl">0.5</span>
)
)
<span class="kw">foreach</span>(<span class="dt">rdfi =</span> <span class="kw">seq_along</span>(researcher_degrees), <span class="dt">.combine =</span> <span class="st">"bind_rows"</span>, <span class="dt">.maxcombine =</span> <span class="dv">1000</span>, <span class="dt">.errorhandling =</span> <span class="st">"remove"</span>) %do%<span class="st"> </span>{
rdf =<span class="st"> </span>researcher_degrees[[rdfi]]
## if you want to see progress in the output file
result =<span class="st"> </span><span class="kw">data.frame</span>(
<span class="dt">nr_of_people =</span> sample_people[i],
<span class="dt">nr_days =</span> nr_of_days[j],
<span class="dt">dayspan =</span> <span class="kw">names</span>(dayspans)[k],
<span class="dt">miss_window =</span> miss_windows[n],
<span class="dt">trait_effect =</span> trait_effects[m],
<span class="dt">fertility_effect =</span> fertility_effects[l],
<span class="dt">rdf =</span> <span class="kw">names</span>(researcher_degrees)[rdfi],
<span class="dt">estimate =</span> <span class="ot">NA_real_</span>,
<span class="dt">std.error =</span> <span class="ot">NA_real_</span>,
<span class="dt">statistic =</span> <span class="ot">NA_real_</span>,
<span class="dt">p.value =</span> <span class="ot">NA_real_</span>,
<span class="dt">usable_days =</span> <span class="ot">NA_real_</span>,
<span class="dt">covariate_used =</span> <span class="ot">NA_real_</span>,
<span class="dt">outcome =</span> <span class="ot">NA_real_</span>,
<span class="dt">predictor =</span> <span class="ot">NA_character_</span>,
<span class="dt">stopping.n =</span> <span class="ot">NA_real_</span>
)
<span class="kw">try</span>({
fit_results =<span class="st"> </span><span class="kw">rdf</span>(simulated_data)
result[, <span class="kw">names</span>(fit_results)] =<span class="st"> </span>fit_results
}, <span class="dt">silent =</span> T)
result
}
}
}
<span class="kw">saveRDS</span>(resdf,<span class="dt">file =</span> <span class="st">"rdf_sims.rds"</span>)
<span class="kw">closeCluster</span>(cl)
<span class="kw">mpi.quit</span>()</code></pre></div>
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