Some work on example code, pkgdown update

2 files changed, 31 insertions, 17 deletions

diff --git a/man/saem.Rd b/man/saem.Rd
index b2daf419..56b54fbf 100644
--- a/man/saem.Rd
+++ b/man/saem.Rd
@@ -81,6 +81,10 @@ f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
 # functions from saemix
 library(saemix)
 compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so))
+plot(f_saem_fomc$so, plot.type = "convergence")
+plot(f_saem_fomc$so, plot.type = "individual.fit")
+plot(f_saem_fomc$so, plot.type = "npde")
+plot(f_saem_fomc$so, plot.type = "vpc")
 
 f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
 f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
@@ -101,11 +105,12 @@ f_mmkin <- mmkin(list(
 # analytical solutions written for saemix. When using the analytical
 # solutions written for mkin this took around four minutes
 f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
-f_saem_dfop_sfo <- saem(f_mmkin["SFO-SFO", ])
+f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
+summary(f_saem_dfop_sfo, data = FALSE)
 
 # Using a single core, the following takes about 6 minutes, using 10 cores
 # it is slower instead of faster
-f_saem_fomc <- saem(f_mmkin["FOMC-SFO", ], cores = 1)
+#f_saem_fomc <- saem(f_mmkin["FOMC-SFO", ], cores = 1)
 }
 }
 \seealso{
diff --git a/man/summary.saem.mmkin.Rd b/man/summary.saem.mmkin.Rd
index 0f0d8264..828691e6 100644
--- a/man/summary.saem.mmkin.Rd
+++ b/man/summary.saem.mmkin.Rd
@@ -51,25 +51,34 @@ endpoints such as formation fractions and DT50 values. Optionally
 (default is FALSE), the data are listed in full.
 }
 \examples{
-# Generate five datasets following SFO kinetics
+# Generate five datasets following DFOP-SFO kinetics
 sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-dt50_sfo_in_pop <- 50
-k_in_pop <- log(2) / dt50_sfo_in_pop
+dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"),
+ m1 = mkinsub("SFO"), quiet = TRUE)
 set.seed(1234)
-k_in <- rlnorm(5, log(k_in_pop), 0.5)
-SFO <- mkinmod(parent = mkinsub("SFO"))
+k1_in <- rlnorm(5, log(0.1), 0.3)
+k2_in <- rlnorm(5, log(0.02), 0.3)
+g_in <- plogis(rnorm(5, qlogis(0.5), 0.3))
+f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3))
+k_m1_in <- rlnorm(5, log(0.02), 0.3)
 
-pred_sfo <- function(k) {
-  mkinpredict(SFO,
-    c(k_parent = k),
-    c(parent = 100),
+pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {
+  mkinpredict(dfop_sfo,
+    c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),
+    c(parent = 100, m1 = 0),
     sampling_times)
 }
 
-ds_sfo_mean <- lapply(k_in, pred_sfo)
-names(ds_sfo_mean) <- paste("ds", 1:5)
+ds_mean_dfop_sfo <- lapply(1:5, function(i) {
+  mkinpredict(dfop_sfo,
+    c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],
+      f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),
+    c(parent = 100, m1 = 0),
+    sampling_times)
+})
+names(ds_mean_dfop_sfo) <- paste("ds", 1:5)
 
-ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {
+ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {
   add_err(ds,
     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
     n = 1)[[1]]
@@ -77,9 +86,9 @@ ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {
 
 \dontrun{
 # Evaluate using mmkin and saem
-f_mmkin <- mmkin("SFO", ds_sfo_syn, quiet = TRUE, error_model = "tc", cores = 1)
-f_saem <- saem(f_mmkin)
-summary(f_saem, data = TRUE)
+f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, quiet = TRUE, error_model = "tc", cores = 5)
+f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
+summary(f_saem_dfop_sfo, data = TRUE)
 }
 
 }