Fit one or more kinetic models with one or more state variables to one or more datasets

+ +

This function calls mkinfit on all combinations of models and +datasets specified in its first two arguments.

+ +

Usage

mmkin(
+  models = c("SFO", "FOMC", "DFOP"),
+  datasets,
+  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
+  cluster = NULL,
+  ...
+)
+
+# S3 method for class 'mmkin'
+print(x, ...)

+ +

Arguments

+ + +

models: Either a character vector of shorthand names like +c("SFO", "FOMC", "DFOP", "HS", "SFORB"), or an optionally named +list of mkinmod objects.
datasets: An optionally named list of datasets suitable as observed +data for mkinfit.
cores: The number of cores to be used for multicore processing. This +is only used when the cluster argument is NULL. On Windows +machines, cores > 1 is not supported, you need to use the cluster +argument to use multiple logical processors. Per default, all cores +detected by parallel::detectCores() are used, except on Windows where +the default is 1.
cluster: A cluster as returned by makeCluster to be used +for parallel execution.
...: Not used.
x: An mmkin object.

Value

A two-dimensional array of mkinfit +objects and/or try-errors that can be indexed using the model names for the +first index (row index) and the dataset names for the second index (column +index).

Author

Johannes Ranke

+ +

Examples


+# \dontrun{
+m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
+                           M1 = mkinsub("SFO", "M2"),
+                           M2 = mkinsub("SFO"), use_of_ff = "max")
+#> Temporary DLL for differentials generated and loaded
+
+m_synth_FOMC_lin <- mkinmod(parent = mkinsub("FOMC", "M1"),
+                            M1 = mkinsub("SFO", "M2"),
+                            M2 = mkinsub("SFO"), use_of_ff = "max")
+#> Temporary DLL for differentials generated and loaded
+
+models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin)
+datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data)
+names(datasets) <- paste("Dataset", 1:3)
+
+time_default <- system.time(fits.0 <- mmkin(models, datasets, quiet = TRUE))
+time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))
+
+time_default
+#>    user  system elapsed 
+#>   1.522   0.957   0.720 
+time_1
+#>    user  system elapsed 
+#>   1.991   0.024   2.015 
+
+endpoints(fits.0[["SFO_lin", 2]])
+#> $ff
+#>   parent_M1 parent_sink       M1_M2     M1_sink 
+#>   0.7340481   0.2659519   0.7505690   0.2494310 
+#> 
+#> $distimes
+#>              DT50       DT90
+#> parent  0.8777689   2.915885
+#> M1      2.3257403   7.725942
+#> M2     33.7201060 112.015767
+#> 
+
+# plot.mkinfit handles rows or columns of mmkin result objects
+plot(fits.0[1, ])
+
+plot(fits.0[1, ], obs_var = c("M1", "M2"))
+
+plot(fits.0[, 1])
+
+# Use double brackets to extract a single mkinfit object, which will be plotted
+# by plot.mkinfit and can be plotted using plot_sep
+plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)
+
+plot_sep(fits.0[[1, 1]])
+# Plotting with mmkin (single brackets, extracting an mmkin object) does not
+# allow to plot the observed variables separately
+plot(fits.0[1, 1])
+
+
+# On Windows, we can use multiple cores by making a cluster first
+cl <- parallel::makePSOCKcluster(12)
+f <- mmkin(c("SFO", "FOMC", "DFOP"),
+  list(A = FOCUS_2006_A, B = FOCUS_2006_B, C = FOCUS_2006_C, D = FOCUS_2006_D),
+  cluster = cl, quiet = TRUE)
+print(f)
+#> <mmkin> object
+#> Status of individual fits:
+#> 
+#>       dataset
+#> model  A  B  C  D 
+#>   SFO  OK OK OK OK
+#>   FOMC C  OK OK OK
+#>   DFOP OK OK OK OK
+#> 
+#> C: Optimisation did not converge:
+#> false convergence (8)
+#> OK: No warnings
+# We get false convergence for the FOMC fit to FOCUS_2006_A because this
+# dataset is really SFO, and the FOMC fit is overparameterised
+parallel::stopCluster(cl)
+# }
+
+