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<h1>Synthetic data for hierarchical kinetic degradation models</h1>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ds_mixed.R" class="external-link"><code>R/ds_mixed.R</code></a></small>
<div class="hidden name"><code>ds_mixed.Rd</code></div>
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<div class="ref-description">
<p>The R code used to create this data object is installed with this package in
the 'dataset_generation' directory.</p>
</div>
<div id="ref-examples">
<h2>Examples</h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
<span class="r-in"><span> <span class="va">sfo_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">sfo_saem</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">sfo_mmkin</span>, no_random_effect <span class="op">=</span> <span class="st">"parent_0"</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">sfo_saem</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="ds_mixed-1.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># This is the code used to generate the datasets</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/readLines.html" class="external-link">readLines</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span><span class="st">"dataset_generation/ds_mixed.R"</span>, package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span><span class="op">)</span>, sep <span class="op">=</span> <span class="st">"\n"</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> # Synthetic data for hierarchical kinetic models</span>
<span class="r-out co"><span class="r-pr">#></span> # Refactored version of the code previously in tests/testthat/setup_script.R</span>
<span class="r-out co"><span class="r-pr">#></span> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it</span>
<span class="r-out co"><span class="r-pr">#></span> # is always 15 for consistency</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> library(mkin) # We use mkinmod and mkinpredict</span>
<span class="r-out co"><span class="r-pr">#></span> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</span>
<span class="r-out co"><span class="r-pr">#></span> n <- 15</span>
<span class="r-out co"><span class="r-pr">#></span> log_sd <- 0.3</span>
<span class="r-out co"><span class="r-pr">#></span> err_1 = list(const = 1, prop = 0.05)</span>
<span class="r-out co"><span class="r-pr">#></span> tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)</span>
<span class="r-out co"><span class="r-pr">#></span> const <- function(value) 2</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> SFO <- mkinmod(parent = mkinsub("SFO"))</span>
<span class="r-out co"><span class="r-pr">#></span> sfo_pop <- list(parent_0 = 100, k_parent = 0.03)</span>
<span class="r-out co"><span class="r-pr">#></span> sfo_parms <- as.matrix(data.frame(</span>
<span class="r-out co"><span class="r-pr">#></span> k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd)))</span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> ds_sfo <- lapply(1:n, function(i) {</span>
<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(SFO, sfo_parms[i, ],</span>
<span class="r-out co"><span class="r-pr">#></span> c(parent = sfo_pop$parent_0), sampling_times)</span>
<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span>
<span class="r-out co"><span class="r-pr">#></span> })</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_sfo, "pop") <- sfo_pop</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_sfo, "parms") <- sfo_parms</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> FOMC <- mkinmod(parent = mkinsub("FOMC"))</span>
<span class="r-out co"><span class="r-pr">#></span> fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8)</span>
<span class="r-out co"><span class="r-pr">#></span> fomc_parms <- as.matrix(data.frame(</span>
<span class="r-out co"><span class="r-pr">#></span> alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),</span>
<span class="r-out co"><span class="r-pr">#></span> beta = rlnorm(n, log(fomc_pop$beta), 0.2)))</span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> ds_fomc <- lapply(1:n, function(i) {</span>
<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(FOMC, fomc_parms[i, ],</span>
<span class="r-out co"><span class="r-pr">#></span> c(parent = fomc_pop$parent_0), sampling_times)</span>
<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span>
<span class="r-out co"><span class="r-pr">#></span> })</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_fomc, "pop") <- fomc_pop</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_fomc, "parms") <- fomc_parms</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> DFOP <- mkinmod(parent = mkinsub("DFOP"))</span>
<span class="r-out co"><span class="r-pr">#></span> dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)</span>
<span class="r-out co"><span class="r-pr">#></span> dfop_parms <- as.matrix(data.frame(</span>
<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(dfop_pop$k1), log_sd),</span>
<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(dfop_pop$k2), log_sd),</span>
<span class="r-out co"><span class="r-pr">#></span> g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))</span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> ds_dfop <- lapply(1:n, function(i) {</span>
<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],</span>
<span class="r-out co"><span class="r-pr">#></span> c(parent = dfop_pop$parent_0), sampling_times)</span>
<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span>
<span class="r-out co"><span class="r-pr">#></span> })</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop, "pop") <- dfop_pop</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop, "parms") <- dfop_parms</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> HS <- mkinmod(parent = mkinsub("HS"))</span>
<span class="r-out co"><span class="r-pr">#></span> hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)</span>
<span class="r-out co"><span class="r-pr">#></span> hs_parms <- as.matrix(data.frame(</span>
<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(hs_pop$k1), log_sd),</span>
<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(hs_pop$k2), log_sd),</span>
<span class="r-out co"><span class="r-pr">#></span> tb = rlnorm(n, log(hs_pop$tb), 0.1)))</span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> ds_hs <- lapply(1:n, function(i) {</span>
<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(HS, hs_parms[i, ],</span>
<span class="r-out co"><span class="r-pr">#></span> c(parent = hs_pop$parent_0), sampling_times)</span>
<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, const, n = 1)[[1]]</span>
<span class="r-out co"><span class="r-pr">#></span> })</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_hs, "pop") <- hs_pop</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_hs, "parms") <- hs_parms</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> DFOP_SFO <- mkinmod(</span>
<span class="r-out co"><span class="r-pr">#></span> parent = mkinsub("DFOP", "m1"),</span>
<span class="r-out co"><span class="r-pr">#></span> m1 = mkinsub("SFO"),</span>
<span class="r-out co"><span class="r-pr">#></span> quiet = TRUE)</span>
<span class="r-out co"><span class="r-pr">#></span> dfop_sfo_pop <- list(parent_0 = 100,</span>
<span class="r-out co"><span class="r-pr">#></span> k_m1 = 0.007, f_parent_to_m1 = 0.5,</span>
<span class="r-out co"><span class="r-pr">#></span> k1 = 0.1, k2 = 0.02, g = 0.5)</span>
<span class="r-out co"><span class="r-pr">#></span> dfop_sfo_parms <- as.matrix(data.frame(</span>
<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd),</span>
<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd),</span>
<span class="r-out co"><span class="r-pr">#></span> g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)),</span>
<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 = plogis(rnorm(n,</span>
<span class="r-out co"><span class="r-pr">#></span> qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),</span>
<span class="r-out co"><span class="r-pr">#></span> k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd)))</span>
<span class="r-out co"><span class="r-pr">#></span> ds_dfop_sfo_mean <- lapply(1:n,</span>
<span class="r-out co"><span class="r-pr">#></span> function(i) {</span>
<span class="r-out co"><span class="r-pr">#></span> mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ],</span>
<span class="r-out co"><span class="r-pr">#></span> c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times)</span>
<span class="r-out co"><span class="r-pr">#></span> }</span>
<span class="r-out co"><span class="r-pr">#></span> )</span>
<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span>
<span class="r-out co"><span class="r-pr">#></span> ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) {</span>
<span class="r-out co"><span class="r-pr">#></span> add_err(ds,</span>
<span class="r-out co"><span class="r-pr">#></span> sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),</span>
<span class="r-out co"><span class="r-pr">#></span> n = 1, secondary = "m1")[[1]]</span>
<span class="r-out co"><span class="r-pr">#></span> })</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop_sfo, "pop") <- dfop_sfo_pop</span>
<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop_sfo, "parms") <- dfop_sfo_parms</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)</span>
</code></pre></div>
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