Fit nonlinear mixed models with SAEM

+ + +

This function uses saemix::saemix() as a backend for fitting nonlinear mixed +effects models created from mmkin row objects using the stochastic approximation +to the expectation maximisation algorithm (SAEM).

+ +

saem(object, control, ...)
+
+# S3 method for mmkin
+saem(
+  object,
+  control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs =
+    FALSE),
+  cores = 1,
+  verbose = FALSE,
+  suppressPlot = TRUE,
+  ...
+)
+
+saemix_model(object, cores = 1, verbose = FALSE, ...)
+
+saemix_data(object, verbose = FALSE, ...)

+ +

Arguments

+ + + + + + + + + + + + + + + + + + + + + + + + + + +

object	An mmkin row object containing several fits of the same +mkinmod model to different datasets
control	Passed to saemix::saemix
...	Further parameters passed to saemix::saemixData +and saemix::saemixModel.
cores	The number of cores to be used for multicore processing using +`parallel::mclapply()`. Using more than 1 core is experimental and may +lead to uncontrolled forking, apparently depending on the BLAS version +used.
verbose	Should we print information about created objects?
suppressPlot	Should we suppress any plotting that is done +by the saemix function?

+ +

Value

+ +

An saemix::SaemixObject.

An saemix::SaemixModel object.

An saemix::SaemixData object.

Details

+ +

An mmkin row object is essentially a list of mkinfit objects that have been +obtained by fitting the same model to a list of datasets using mkinfit.

Starting values for the fixed effects (population mean parameters, argument +psi0 of saemix::saemixModel() are the mean values of the parameters found +using mmkin.

+ +

Examples

# \dontrun{
+ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")]))
+names(ds) <- paste("Dataset", 6:10)
+f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, cores = 1,
+  state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
+f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
+
#> Running main SAEM algorithm
+#> [1] "Sat Nov  7 13:14:50 2020"
+#> ....
+#>     Minimisation finished
+#> [1] "Sat Nov  7 13:14:52 2020"

+f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
+f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
+
#> Running main SAEM algorithm
+#> [1] "Sat Nov  7 13:14:53 2020"
+#> ....
+#>     Minimisation finished
+#> [1] "Sat Nov  7 13:14:55 2020"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
+
#> Running main SAEM algorithm
+#> [1] "Sat Nov  7 13:14:55 2020"
+#> ....
+#>     Minimisation finished
+#> [1] "Sat Nov  7 13:14:57 2020"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
+
#> Running main SAEM algorithm
+#> [1] "Sat Nov  7 13:14:57 2020"
+#> ....
+#>     Minimisation finished
+#> [1] "Sat Nov  7 13:15:00 2020"

+# The returned saem.mmkin object contains an SaemixObject, we can use
+# functions from saemix
+library(saemix)
+
#> Package saemix, version 3.1.9000
+#>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so))
+
#> Likelihoods computed by importance sampling 
#>        AIC      BIC
+#> 1 624.2428 622.2900
+#> 2 467.7644 465.0305
+#> 3 491.3541 487.8391

+f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
+f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
+
#> Running main SAEM algorithm
+#> [1] "Sat Nov  7 13:15:02 2020"
+#> ....
+#>     Minimisation finished
+#> [1] "Sat Nov  7 13:15:07 2020"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
+
#> Likelihoods computed by importance sampling 
#>        AIC      BIC
+#> 1 467.7644 465.0305
+#> 2 469.4862 466.3617

+dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+  A1 = mkinsub("SFO"))
+
#> Successfully compiled differential equation model from auto-generated C code.
f_mmkin <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, solution_type = "analytical")
+# This takes about 4 minutes on my system
+f_saem <- saem(f_mmkin)
+
#> Running main SAEM algorithm
+#> [1] "Sat Nov  7 13:15:08 2020"
+#> ....
+#>     Minimisation finished
+#> [1] "Sat Nov  7 13:19:07 2020"

+f_mmkin_des <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, solution_type = "deSolve")
+# Using a single core, the following takes about 6 minutes, using 10 cores
+# it is slower instead of faster
+f_saem_des <- saem(f_mmkin_des, cores = 1)
+
#> Running main SAEM algorithm
+#> [1] "Sat Nov  7 13:19:26 2020"
+#> ....
+#>     Minimisation finished
+#> [1] "Sat Nov  7 13:27:33 2020"
compare.saemix(list(f_saemix$so, f_saemix_des$so))
+
#> Error in compare.saemix(list(f_saemix$so, f_saemix_des$so)): object 'f_saemix' not found
# }
+

+ +