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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> + </div> + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + |