This function will provide fit index cutoffs for values of alpha, and mean fit index values across all replications.
1  summaryFit(object, alpha = NULL, improper = FALSE, usedFit = NULL)

object 

alpha 
The alpha level used to find the fit indices cutoff. If there is no varying condition, a vector of different alpha levels can be provided. 
improper 
If TRUE, include the replications that provided improper solutions 
usedFit 
Vector of names of fit indices that researchers wish to summarize. 
A data frame that provides fit statistics cutoffs and means
When linkS4class{SimResult}
has fixed simulation parameters the first colmns are fit index cutoffs for values of alpha and the last column is the mean fit across all replications. Rows are
Chi Chisquare fit statistic
AIC Akaike Information Criterion
BIC Baysian Information Criterion
RMSEA Root Mean Square Error of Approximation
CFI Comparative Fit Index
TLI TuckerLewis Index
SRMR Standardized Root Mean Residual
When linkS4class{SimResult}
has random simulation parameters (sample size or percent missing), columns are the fit indices listed above and rows are values of the random parameter.
Alexander M. Schoemann (East Carolina University; schoemanna@ecu.edu) Sunthud Pornprasertmanit (psunthud@gmail.com)
SimResult
for the result object input
1 2 3 4 5 6 7 8 9 10 11 12 13  loading < matrix(0, 6, 1)
loading[1:6, 1] < NA
LY < bind(loading, 0.7)
RPS < binds(diag(1))
RTE < binds(diag(6))
CFA.Model < model(LY = LY, RPS = RPS, RTE = RTE, modelType="CFA")
# We make the examples running only 5 replications to save time.
# In reality, more replications are needed.
Output < sim(5, n=500, CFA.Model)
# Summarize the sample fit indices
summaryFit(Output)

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