extractModelSummaries: (DEPRECATED) Extract summary statistics from a single output...

In MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

(DEPRECATED) 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 https://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\

• RMSEA_90CI_UB: Upper bound of the 90\

• 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\

• ObsRepChiSqDiff_95CI_UB: Upper bound of 95\

• 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)

MplusAutomation documentation built on Sept. 9, 2025, 5:25 p.m.