# 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. ```r 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. ## Returns 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). ## Examples ```r ## 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) ``` ## See Also `[.mmkin` for subsetting, `plot.mmkin` for plotting. ## Author(s) Johannes Ranke

mmkin function