Create a summary table of Mplus mixture models

Creates a summary table of model fit statistics and relevant diagnostic information for a list of mixture models. Default statistics reported are in line with published guidelines (see Jung & Wickrama, 2008; Nylund et al., 2007): c("Title", "Classes", "Warnings", "AIC", "BIC", "aBIC", "Entropy", "T11_VLMR_PValue", "T11_LMR_PValue", "BLRT_PValue", "min_N", "max_N", "min_prob", "max_prob"). The table is customizable using the keepCols parameter, which is passed through to SummaryTable.

mixtureSummaryTable(
  modelList,
  keepCols = c("Title", "Classes", "Warnings", "AIC", "BIC", "aBIC", "Entropy",
    "T11_VLMR_PValue", "T11_LMR_PValue", "BLRT_PValue", "min_N", "max_N", "min_prob",
    "max_prob"),
  sortBy = NULL,
  ...
)

Arguments

modelList	A list of models returned from the extractModelSummaries function.
keepCols	A vector of character strings indicating which columns/variables to display in the summary. Only columns included in this list will be displayed (all others excluded). By default, keepCols is: c("Title", "Classes", "Warnings", "AIC", "BIC", "aBIC","Entropy", "T11_VLMR_PValue", "T11_LMR_PValue", "BLRT_PValue", "min_N", "max_N", "min_prob", "max_prob").
sortBy	Field name (as character string) by which to sort the table. Typically an information criterion (e.g., "AIC" or "BIC") is used to sort the table. Defaults to "AICC". Set to NULL by default, so the table is ordered by increasing number of classes.
...	Arguments passed to SummaryTable.

Value

An object of class data.frame.

Note

This function is partially a wrapper around SummaryTable, with enhancements for summarizing mixture models.

See also

SummaryTable

Author

Caspar J. van Lissa

Examples

if (FALSE) {
res <- createMixtures(classes = 1:2, filename_stem = "iris", rdata = iris,
               OUTPUT = "tech11 tech14;",
               run = 1L)
mixtureSummaryTable(res)
}