Fast analytical solutions for saemix, update.mmkin

Also, use logit transformation for g and for solitary formation
fractions, addressing #10.

1 files changed, 27 insertions, 34 deletions

diff --git a/man/saemix.Rd b/man/saemix.Rd
index 962526ba..d4a8d0a4 100644
--- a/man/saemix.Rd
+++ b/man/saemix.Rd
@@ -38,46 +38,39 @@ mmkin. Starting variances of the random effects (argument omega.init) are the
 variances of the deviations of the parameters from these mean values.
 }
 \examples{
+\dontrun{
+library(saemix)
 ds <- lapply(experimental_data_for_UBA_2019[6:10],
  function(x) subset(x$data[c("name", "time", "value")]))
 names(ds) <- paste("Dataset", 6:10)
-sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, cores = 1,
+  state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
+m_saemix_p0_fixed <- saemix_model(f_mmkin_parent_p0_fixed["FOMC", ])
+d_saemix_parent <- saemix_data(f_mmkin_parent_p0_fixed)
+saemix_options <- list(seed = 123456, displayProgress = FALSE,
+  save = FALSE, save.graphs = FALSE, nbiter.saemix = c(200, 80))
+f_saemix_p0_fixed <- saemix(m_saemix_p0_fixed, d_saemix_parent, saemix_options)
+
+f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
+m_saemix_sfo <- saemix_model(f_mmkin_parent["SFO", ])
+m_saemix_fomc <- saemix_model(f_mmkin_parent["FOMC", ])
+m_saemix_dfop <- saemix_model(f_mmkin_parent["DFOP", ])
+d_saemix_parent <- saemix_data(f_mmkin_parent["SFO", ])
+f_saemix_sfo <- saemix(m_saemix_sfo, d_saemix_parent, saemix_options)
+f_saemix_fomc <- saemix(m_saemix_fomc, d_saemix_parent, saemix_options)
+f_saemix_dfop <- saemix(m_saemix_dfop, d_saemix_parent, saemix_options)
+compare.saemix(list(f_saemix_sfo, f_saemix_fomc, f_saemix_dfop))
+f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
+m_saemix_fomc_tc <- saemix_model(f_mmkin_parent_tc["FOMC", ])
+f_saemix_fomc_tc <- saemix(m_saemix_fomc_tc, d_saemix_parent, saemix_options)
+compare.saemix(list(f_saemix_fomc, f_saemix_fomc_tc))
+
+dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
   A1 = mkinsub("SFO"))
-\dontrun{
-f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE)
-library(saemix)
-m_saemix <- saemix_model(f_mmkin, cores = 1)
+f_mmkin <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)
+m_saemix <- saemix_model(f_mmkin)
 d_saemix <- saemix_data(f_mmkin)
-saemix_options <- list(seed = 123456,
-  save = FALSE, save.graphs = FALSE, displayProgress = FALSE,
-  nbiter.saemix = c(200, 80))
 f_saemix <- saemix(m_saemix, d_saemix, saemix_options)
-plot(f_saemix, plot.type = "convergence")
-}
-# Synthetic data with two-component error
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-dt50_sfo_in <- c(80, 90, 100, 111.111, 125)
-k_in <- log(2) / dt50_sfo_in
-
-SFO <- mkinmod(parent = mkinsub("SFO"))
 
-pred_sfo <- function(k) {
-  mkinpredict(SFO, c(k_parent = k),
-    c(parent = 100), sampling_times)
-}
-
-ds_sfo_mean <- lapply(k_in, pred_sfo)
-set.seed(123456L)
-ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {
-  add_err(ds, sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
-     n = 1)[[1]]
- })
-\dontrun{
-f_mmkin_syn <- mmkin("SFO", ds_sfo_syn, error_model = "tc", quiet = TRUE)
-# plot(f_mmkin_syn)
-m_saemix_tc <- saemix_model(f_mmkin_syn, cores = 1)
-d_saemix_tc <- saemix_data(f_mmkin_syn)
-f_saemix_tc <- saemix(m_saemix_tc, d_saemix_tc, saemix_options)
-plot(f_saemix_tc, plot.type = "convergence")
 }
 }