extractModelSummaries {MplusAutomation}

Extract summary statistics from a single output file or from a group of Mplus models within a directory

Description

Parses a group of Mplus model output files (.out extension) for model fit statistics. At this time, the details extracted are fixed and include: Filename, InputInstructions, Title, Estimator, LL, BIC, aBIC, AIC, AICC, Parameters, Observations, CFI, TLI, RMSEA_Estimate, RMSEA_90CI_LB, RMSEA_90CI_UB, RMSEA_pLT05, ChiSqM_Value, ChiSqM_DF, ChiSq_PValue, BLRT_KM1LL, BLRT_PValue, BLRT_Numdraws). The infrastructure is in place to allow for user-specified selection of summary statistics in future versions.

Usage

extractModelSummaries(target = getwd(), recursive = FALSE, filefilter)

Arguments

target

the directory containing Mplus output files (.out) to parse OR the single output file to be parsed. Defaults to the current working directory. Example: "C:/Users/Michael/Mplus Runs"

recursive

optional. If TRUE, parse all models nested in subdirectories within directory. 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.

Value

Returns a data.frame containing model fit statistics for all output files within directory. The data.frame contains some of the following variables (depends on model type):

Title

Title for the model, specified by the TITLE: command

Filename

Filename of the output file

Estimator

Estimator used for the model (e.g., ML, MLR, WLSMV, etc.)

LL

Log-likelihood of the model

BIC

Bayesian Information Criterion

aBIC

Sample-Size-Adjusted BIC (Sclove, 1987)

AIC

Akaike's Information Criterion

AICC

Corrected AIC, based on Sugiura (1978) and recommended by Burnham & Anderson (2002)

DIC

Deviance Information Criterion. Available in ESTIMATOR=BAYES output.

Parameters

Number of parameters estimated by the model

pD

Estimated number of parameters in Bayesian output

Observations

The number of observations for the model (does not suppport multiple-groups analysis at this time)

CFI

Confirmatory Fit Index

TLI

Tucker-Lewis Index

RMSEA_Estimate

Point estimate of root mean squared error of approximation

RMSEA_90CI_LB

Lower bound of the 90% Confidence Interval around the RMSEA estimate.

RMSEA_90CI_UB

Upper bound of the 90% Confidence Interval around the RMSEA estimate.

RMSEA_pLT05

Probability that the RMSEA estimate falls below .05, indicating good fit.

ChiSqM_Value

Model chi-squared value

ChiSqM_DF

Model chi-squared degrees of freedom

ChiSqM_PValue

Model chi-squared p value

ChiSqM_ScalingCorrection

H0 Scaling Correction Factor

ObsRepChiSqDiff_95CI_LB

Lower bound of 95% confidence interval for the difference between observed and replicated chi-square values

ObsRepChiSqDiff_95CI_UB

Upper bound of 95% confidence interval for the difference between observed and replicated chi-square values

PostPred_PValue

Posterior predictive p-value

PriorPostPred_PValue

Prior Posterior Predictive P-Value

BLRT_RequestedDraws

Number of requested bootstrap draws for TECH14.

BLRT_KM1LL

Log-likelihood of the K-1 model (one less class) for the Bootstrapped Likelihood Ratio Test (TECH14).

BLRT_2xLLDiff

Two times the log-likelihood difference of the models with K and K-1 classes (TECH14).

BLRT_ParamDiff

Difference in the number of parameters for models with K and K-1 classes (TECH14).

BLRT_PValue

P-value of the Bootstrapped Likelihood Ratio Test (TECH14) testing whether the K class model is significantly better than K-1

BLRT_SuccessfulDraws

The number of successful bootstrapped samples used in the Bootstrapped Likelihood Ratio Test

SRMR

Standardized root mean square residual

SRMR.Between

For TYPE=TWOLEVEL output, standardized root mean square residual for between level

SRMR.Within

For TYPE=TWOLEVEL output, standardized root mean square residual for within level

WRMR

Weighted root mean square residual

ChiSqBaseline_Value

Baseline (unstructured) chi-squared value

ChiSqBaseline_DF

Baseline (unstructured) chi-squared degrees of freedom

ChiSqBaseline_PValue

Baseline (unstructured) chi-squared p value

NumFactors

For TYPE=EFA output, the number of factors

T11_KM1Starts

TECH11: Number of initial stage random starts for k-1 model

T11_KM1Final

TECH11: Number of final stage optimizations for k-1 model

T11_KM1LL

TECH11: Log-likelihood of the K-1 model used for the Vuong-Lo-Mendell-Rubin LRT

T11_VLMR_2xLLDiff

TECH11: 2 * Log-likelihood Difference of K-class vs. K-1-class model for the Vuong-Lo-Mendell-Rubin LRT

T11_VLMR_ParamDiff

TECH11: Difference in number of parameters between K-class and K-1-class model for the Vuong-Lo-Mendell-Rubin LRT

T11_VLMR_Mean

TECH11: Vuong-Lo-Mendell-Rubin LRT mean

T11_VLMR_SD

TECH11: Vuong-Lo-Mendell-Rubin LRT standard deviation

T11_VLMR_PValue

TECH11: Vuong-Lo-Mendell-Rubin LRT p-value

T11_LMR_Value

TECH11: Lo-Mendell-Rubin Adjusted LRT value

T11_LMR_PValue

TECH11: Lo-Mendell-Rubin Adjusted LRT p-value

Author(s)

Michael Hallquist

See Also

regex, runModels, readModels

Examples

## Not run: 
  allExamples <- extractModelSummaries(
    "C:/Program Files/Mplus/Mplus Examples/User's Guide Examples")

## End(Not run)