Description Usage Arguments Value Author(s) See Also Examples
View source: R/getPowerFitNested.R
Find the proportion of the difference in fit indices that indicate worse fit than a specified (or internally derived) cutoffs.
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altNested 

altParent 

cutoff 
A vector of priori cutoffs for fit indices. The 
nullNested 
The 
nullParent 
The 
revDirec 
Reverse the direction of deciding a power by fit indices (e.g., less than –> greater than). The default is to count the proportion of fit indices that indicates lower fit to the model, such as how many RMSEA in the alternative model that is worse than cutoffs. The direction can be reversed by setting as 
usedFit 
The vector of names of fit indices that researchers wish to get powers from. The default is to get powers of all fit indices 
alpha 
The alpha level used to find the cutoff if the 
nVal 
The sample size value that researchers wish to find the power from. This argument is applicable when 
pmMCARval 
The percent missing completely at random value that researchers wish to find the power from. This argument is applicable when 
pmMARval 
The percent missing at random value that researchers wish to find the power from. This argument is applicable when 
condCutoff 
A logical value to use a conditional quantile method (if 
df 
The degree of freedom used in spline method in quantile regression ( 
List of power given different fit indices. The TraditionalChi
means the proportion of replications that are rejected by the traditional chisquare difference test.
Sunthud Pornprasertmanit (psunthud@gmail.com)
getCutoff
to find the cutoffs from null model.
SimResult
to see how to create 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49  ## Not run:
# Null model (Nested model) with one factor
loading.null < matrix(0, 6, 1)
loading.null[1:6, 1] < NA
LY.NULL < bind(loading.null, 0.7)
RPS.NULL < binds(diag(1))
RTE < binds(diag(6))
CFA.Model.NULL < model(LY = LY.NULL, RPS = RPS.NULL, RTE = RTE, modelType="CFA")
# Alternative model (Parent model) with two factors
loading.alt < matrix(0, 6, 2)
loading.alt[1:3, 1] < NA
loading.alt[4:6, 2] < NA
LY.ALT < bind(loading.alt, 0.7)
latent.cor.alt < matrix(NA, 2, 2)
diag(latent.cor.alt) < 1
RPS.ALT < binds(latent.cor.alt, 0.7)
CFA.Model.ALT < model(LY = LY.ALT, RPS = RPS.ALT, RTE = RTE, modelType="CFA")
# We make the examples running only 10 replications to save time.
# In reality, more replications are needed.
Output.NULL.NULL < sim(10, n=500, model=CFA.Model.NULL, generate=CFA.Model.NULL)
Output.ALT.NULL < sim(10, n=500, model=CFA.Model.NULL, generate=CFA.Model.ALT)
Output.NULL.ALT < sim(10, n=500, model=CFA.Model.ALT, generate=CFA.Model.NULL)
Output.ALT.ALT < sim(10, n=500, model=CFA.Model.ALT, generate=CFA.Model.ALT)
# Find the power based on the derived cutoff from the models analyzed on the null datasets
getPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL,
nullParent=Output.NULL.ALT)
# Find the power based on the chisquare value at df=1 and the CFI change (intentionally
# use a cutoff from Cheung and Rensvold (2002) in an appropriate situation).
getPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, cutoff=c(Chi=3.84, CFI=0.10))
# The example of continous varying sample size. Note that more finegrained
# values of n is needed, e.g., n=seq(50, 500, 1)
Output.NULL.NULL2 < sim(NULL, n=seq(50, 500, 50), model=CFA.Model.NULL, generate=CFA.Model.NULL)
Output.ALT.NULL2 < sim(NULL, n=seq(50, 500, 50), model=CFA.Model.NULL, generate=CFA.Model.ALT)
Output.NULL.ALT2 < sim(NULL, n=seq(50, 500, 50), model=CFA.Model.ALT, generate=CFA.Model.NULL)
Output.ALT.ALT2 < sim(NULL, n=seq(50, 500, 50), model=CFA.Model.ALT, generate=CFA.Model.ALT)
# Get the power based on the derived cutoff from the null model at the sample size of 250
getPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, nullNested=Output.NULL.NULL2,
nullParent=Output.NULL.ALT2, nVal = 250)
# Get the power based on the rule of thumb from the null model at the sample size of 250
getPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, cutoff=c(Chi=3.84, CFI=0.10), nVal = 250)
## End(Not run)

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