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.
Usage
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
.
Note
This function is partially a wrapper around SummaryTable, with enhancements for summarizing mixture models.
Examples
if (FALSE) { # \dontrun{
res <- createMixtures(classes = 1:2, filename_stem = "iris", rdata = iris,
OUTPUT = "tech11 tech14;",
run = 1L)
mixtureSummaryTable(res)
} # }