Description Usage Arguments Value Author(s) See Also Examples
Extract fit indices information from the SimResult
and get the cutoffs of fit indices given a priori alpha level
1 2 
object 

alpha 
A priori alpha level 
revDirec 
The default is to find criticl point on the side that indicates worse fit (the right side of RMSEA or the left side of CFI). If specifying as 
usedFit 
Vector of names of fit indices that researchers wish to getCutoffs from. The default is to getCutoffs of all fit indices. 
nVal 
The sample size value that researchers wish to find the fit indices cutoffs from. This argument is applicable for 
pmMCARval 
The percent missing completely at random value that researchers wish to find the fit indices cutoffs from. This argument is applicable for 
pmMARval 
The percent missing at random value that researchers wish to find the fit indices cutoffs from. This argument is applicable for 
df 
The degree of freedom used in spline method in predicting the fit indices by the predictors. If 
Onetailed cutoffs of several fit indices with a priori alpha level
Sunthud Pornprasertmanit (psunthud@gmail.com)
SimResult
for a detail of simResult
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  ## Not run:
loading < matrix(0, 6, 2)
loading[1:3, 1] < NA
loading[4:6, 2] < NA
loadingValues < matrix(0, 6, 2)
loadingValues[1:3, 1] < 0.7
loadingValues[4:6, 2] < 0.7
LY < bind(loading, loadingValues)
latent.cor < matrix(NA, 2, 2)
diag(latent.cor) < 1
RPS < binds(latent.cor, 0.5)
error.cor < matrix(0, 6, 6)
diag(error.cor) < 1
RTE < binds(error.cor)
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 = 200, model=CFA.Model)
# Get the cutoff (critical value) when alpha is 0.05
getCutoff(Output, 0.05)
# Finding the cutoff when the sample size is varied. Note that more finegrained
# values of n is needed, e.g., n=seq(50, 500, 1)
Output2 < sim(NULL, model=CFA.Model, n=seq(50, 100, 10))
# Get the fit index cutoff when sample size is 75.
getCutoff(Output2, 0.05, nVal = 75)
## End(Not run)

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