MplusAutomation (version 0.6-1)

extractModelSummaries: 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
• 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
• BLRT_KM1LL
  Log-likelihood of the K-1 model (one less class) for the Bootstrapped Likelihood Ratio Test (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_Numdraws
  The number of 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

Examples

allExamples <- extractModelSummaries(
    "C:/Program Files/Mplus/Mplus Examples/User's Guide Examples")