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 +

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, ...)

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 + + 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 +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.

An mmkin object.

Value

A two-dimensional array of mkinfitobjects 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

- +

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 
-#>   4.491   0.397   1.634 
time_1
-
#>    user  system elapsed 
-#>   5.491   0.000   5.492 

-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 using the parallel
-# package, which gets loaded with mkin, and passing it to mmkin, e.g.
-cl <- 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
-stopCluster(cl)
-# }
-
-

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 
+#>   6.055   1.405   2.191 
+time_1
+#>    user  system elapsed 
+#>   6.746   0.004   6.750 
+
+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 using the parallel
+# package, which gets loaded with mkin, and passing it to mmkin, e.g.
+cl <- 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
+stopCluster(cl)
+# }
+
+

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

Arguments

Arguments

Value

Value

See also

See also

Author

Author

Examples

Examples