extractModelSummaries: Extract summary statistics from a single output file or from...

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

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)

MplusAutomation documentation built on May 29, 2024, 5:29 a.m.