Extract fit indices information from the simulation of two models fitting on the datasets created from both models and getCutoff of fit indices given a priori alpha level
1 2 3 
dat1Mod1 

dat1Mod2 

dat2Mod1 

dat2Mod2 

alpha 
A priori alpha level 
usedFit 
Vector of names of fit indices that researchers wish to get cutoffs from. The default is to get cutoffs of all fit indices. 
onetailed 
If 
nVal 
The sample size value that researchers wish to find the fit indices cutoffs from. 
pmMCARval 
The percent missing completely at random value that researchers wish to find the fit indices cutoffs from. 
pmMARval 
The percent missing at random value that researchers wish to find the fit indices cutoffs from. 
df 
The degree of freedom used in spline method in predicting the fit indices by the predictors. If 
One or twotailed cutoffs of several fit indices with a priori alpha level. The cutoff is based on the fit indices from Model 1 subtracted by the fit indices from Model 2.
Sunthud Pornprasertmanit (psunthud@gmail.com)
SimResult
for a detail of simResult
getCutoff
for a detail of finding cutoffs for absolute fit
getCutoffNested
for a detail of finding cutoffs for nested model comparison
plotCutoffNonNested
Plot cutoffs for nonnested model comparison
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 32 33 34 35 36  ## Not run:
# Model A: Factor 1 with items 13 and Factor 2 with items 48
loading.A < matrix(0, 8, 2)
loading.A[1:3, 1] < NA
loading.A[4:8, 2] < NA
LY.A < bind(loading.A, 0.7)
latent.cor < matrix(NA, 2, 2)
diag(latent.cor) < 1
RPS < binds(latent.cor, "runif(1, 0.7, 0.9)")
RTE < binds(diag(8))
CFA.Model.A < model(LY = LY.A, RPS = RPS, RTE = RTE, modelType="CFA")
# Model B: Factor 1 with items 14 and Factor 2 with items 58
loading.B < matrix(0, 8, 2)
loading.B[1:4, 1] < NA
loading.B[5:8, 2] < NA
LY.B < bind(loading.B, 0.7)
CFA.Model.B < model(LY = LY.B, RPS = RPS, RTE = RTE, modelType="CFA")
# The actual number of replications should be greater than 10.
Output.A.A < sim(10, n=500, model=CFA.Model.A, generate=CFA.Model.A)
Output.A.B < sim(10, n=500, model=CFA.Model.B, generate=CFA.Model.A)
Output.B.A < sim(10, n=500, model=CFA.Model.A, generate=CFA.Model.B)
Output.B.B < sim(10, n=500, model=CFA.Model.B, generate=CFA.Model.B)
# Find the cutoffs from the sampling distribution to reject model A (model 1)
# and to reject model B (model 2)
getCutoffNonNested(Output.A.A, Output.A.B, Output.B.A, Output.B.B)
# Find the cutoffs from the sampling distribution to reject model A (model 1)
getCutoffNonNested(Output.A.A, Output.A.B)
# Find the cutoffs from the sampling distribution to reject model B (model 1)
getCutoffNonNested(Output.B.B, Output.B.A)
## End(Not run)

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