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.

- -

mmkin(
-  models = c("SFO", "FOMC", "DFOP"),
-  datasets,
-  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
-  cluster = NULL,
-  ...
-)
-
-# S3 method for 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 
-#>   1.560   0.668   0.742 
-time_1
-#>    user  system elapsed 
-#>   2.033   0.004   2.037 
-
-endpoints(fits.0[["SFO_lin", 2]])
-#> $ff
-#>   parent_M1 parent_sink       M1_M2     M1_sink 
-#>   0.7340481   0.2659519   0.7505683   0.2494317 
-#> 
-#> $distimes
-#>             DT50       DT90
-#> parent  0.877769   2.915885
-#> M1      2.325744   7.725956
-#> M2     33.720100 112.015749
-#> 
-
-# 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 OK OK OK OK
-#>   DFOP OK OK OK OK
-#> 
-#> 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)
-# }
-
-

- -

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

Arguments

Value

See also

Author

Examples