Extract model parameters from MODEL RESULTS section.

Extracts the model parameters from the MODEL RESULTS section of one or more Mplus output files. If a particular output file has more than one results section (unstandardized, stdyx, stdy, and/or std), a list will be returned. If the target is a directory, all .out files therein will be parsed and a single list will be returned, where the list elements are named by the output file name. Returned parameters often include the parameter estimate, std. err, param/s.e., and two-tailed p-value.

extractModelParameters(
  target = getwd(),
  recursive = FALSE,
  filefilter,
  dropDimensions = FALSE,
  resultType
)

Arguments

target	the directory containing Mplus output files (.out) to parse OR the single output file to be parsed. May be a full path, relative path, or a filename within the working directory. Defaults to the current working directory. Example: “C:/Users/Michael/Mplus Runs”
recursive	optional. If `TRUE`, parse all models nested in subdirectories within `target`. Defaults to `FALSE`.
filefilter	a Perl regular expression (PCRE-compatible) specifying particular output files to be parsed within `directory`. See `regex` or http://www.pcre.org/pcre.txt for details about regular expression syntax.
dropDimensions	Relevant only for multi-file parsing. If `TRUE`, then if only one output section (usually unstandardized) is present for all files in the parsed list, then eliminate the second-level list (which contains elements for each output section). The result is that the elements of the returned list are `data.frame` objects with the relevant parameters.
resultType	N.B.: this parameter is deprecated and will be removed in a future version. The new default is to extract all results that are present and return a list (see below for details). `resultType` specified the results section to extract. If `raw`, the unstandardized estimates will be returned. “stdyx”, “stdy”, and “std” are the other options, which extract different standardized solutions. See the Mplus User's Guide for additional details about the differences in these standardizations.

Value

If target is a single file, a list containing unstandardized and standardized results will be returned. If all standardized solutions are available, the list element will be named: unstandardized, stdyx.standardized, stdy.standardized, and std.standardized. If confidence intervals are output using OUTPUT:CINTERVAL, then a list element named ci.unstandardized will be included. Each of these list elements is a data.frame containing relevant model parameters.

If target is a directory, a list will be returned, where each element contains the results for a single file, and the top-level elements are named after the corresponding output file name. Each element within this list is itself a list, with elements as in the single file case above.

The core data.frame for each MODEL RESULTS section typically has the following structure:

paramHeader

The header that begins a given parameter set. Example: "FACTOR1 BY"

param

The particular parameter being measured (within paramHeader). Example: "ITEM1"

est

Parameter estimate value.

Standard error of the estimate

est_se

Quotient of est/se, representing z-test/t-test in large samples

pval

Two-tailed p-value for the est_se quotient.

In the case of output from Bayesian estimation (ESTIMATOR=BAYES), the data.frame will contain a different set of variables, including some of the above, as well as

posterior_sd

Posterior standard deviation of the estimate.

lower_2.5ci

Lower 2.5 percentile of the estimate.

upper_2.5ci

Upper 2.5 percentile (aka 97.5 percentile) of the estimate.

Also note that the pval column for Bayesian output represents a one-tailed estimate. In the case of output from a Monte Carlo study (MONTECARLO: and MODEL POPULATION:), the data.frame will contain a different set of variables, including some of the above, as well as

population

Population parameter value.

average

Average parameter estimate across replications.

population_sd

Standard deviation of parameter value in population across replications.

average_se

Average standard error of estimated parameter value across replications.

mse

Mean squared error.

cover_95

Proportion of replications whose 95% confidence interval for the parameter includes the population value.

pct_sig_coef

Proportion of replications for which the two-tailed significance test of the parameter is significant (p < .05).

In the case of confidence interval output (OUTPUT:CINTERVAL), the list element ci.unstandardized will contain a different set of variables, including some of the above, as well as

low.5

Lower 0.5% CI estimate.

low2.5

Lower 2.5% CI estimate.

low5

Lower 5% CI estimate.

est

Parameter estimate value.

up5

Upper 5% (i.e., 95%) CI estimate.

up2.5

Upper 2.5% (i.e., 97.5%) CI estimate.

up.5

Upper 0.5% (i.e., 99.5%) CI estimate.

If the model contains multiple latent classes, an additional variable, LatentClass, will be included, specifying the latent class number. Also, the Categorical Latent Variables section will be included as LatentClass "Categorical.Latent.Variables." If the model contains multiple groups, Group will be included. If the model contains two-level output (between/within), BetweenWithin will be included.

Author

Michael Hallquist

Examples

if (FALSE) {
ex3.14 <- extractModelParameters(
  "C:/Program Files/Mplus/Mplus Examples/User's Guide Examples/ex3.14.out")
}

Arguments

Value

See also

Author

Examples