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

mmkin(models, datasets,
      cores = round(detectCores()/2), cluster = NULL, ...)



Either a character vector of shorthand names ("SFO", "FOMC", "DFOP", "HS", "SFORB"), or an optionally named list of mkinmod objects.


An optionally named list of datasets suitable as observed data for mkinfit.


The number of cores to be used for multicore processing. This is only used when the cluster argument is NULL.


A cluster as returned by makeCluster to be used for parallel execution.

Further arguments that will be passed to mkinfit.


A matrix of mkinfit objects that can be indexed using the model and dataset names as row and column indices.

See also

[.mmkin for subsetting, plot.mmkin for plotting.


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))


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])
# }