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 class 'mmkin' print(x, ...)
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.
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).
## Not run: m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"), M1 = mkinsub("SFO", "M2"), M2 = mkinsub("SFO"), use_of_ff = "max") m_synth_FOMC_lin <- mkinmod(parent = mkinsub("FOMC", "M1"), M1 = mkinsub("SFO", "M2"), M2 = mkinsub("SFO"), use_of_ff = "max") 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 time_1 endpoints(fits.0[["SFO_lin", 2]]) # 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) # 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) ## End(Not run)
[.mmkin for subsetting, plot.mmkin for plotting.
Johannes Ranke