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

Source: R/parseOutput.R
extractModelSummaries.Rd

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

See also

regex, runModels, readModels

Author

Michael Hallquist

Examples

if (FALSE) { # \dontrun{
  allExamples <- extractModelSummaries(
    "C:/Program Files/Mplus/Mplus Examples/User's Guide Examples")
} # }