Description Usage Arguments Value Author(s) See Also Examples
View source: R/plotPowerFitNested.R
This function will plot sampling distributions of the differences in fit indices between parent and nested models. Two sampling distributions will be compared: nested model is FALSE
(alternative model) and nested model is TRUE
(null model).
1 2 3 4 
altNested 

altParent 

nullNested 

nullParent 

cutoff 
A vector of priori cutoffs for the differences in fit indices. 
usedFit 
Vector of names of fit indices that researchers wish to plot. 
alpha 
A priori alpha level 
contN 
Include the varying sample size in the power plot if available 
contMCAR 
Include the varying MCAR (missing completely at random percentage) in the power plot if available 
contMAR 
Include the varying MAR (missing at random percentage) in the power plot if available 
useContour 
If there are two of sample size, percent completely at random, and percent missing at random are varying, the 
logistic 
If 
NONE. Only plot the fit indices distributions.
Sunthud Pornprasertmanit (psunthud@gmail.com)
SimResult
for simResult that used in this function.
getCutoffNested
to find the cutoffs of the differences in fit indices
plotCutoffNested
to visualize the cutoffs of the differences in fit indices
getPowerFitNested
to find the power in rejecting the nested model by the difference in fit indices cutoffs
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 50 51 52 53 54  ## Not run:
# Null model: Onefactor model
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: Twofactor model
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")
# In reality, more than 10 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)
# Plot the power based on the derived cutoff from the models analyzed on the null datasets
plotPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL,
nullParent=Output.NULL.ALT)
# Plot the power by only CFI
plotPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL,
nullParent=Output.NULL.ALT, usedFit="CFI")
# 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, 5), model=CFA.Model.NULL, generate=CFA.Model.NULL)
Output.ALT.NULL2 < sim(NULL, n=seq(50, 500, 5), model=CFA.Model.NULL, generate=CFA.Model.ALT)
Output.NULL.ALT2 < sim(NULL, n=seq(50, 500, 5), model=CFA.Model.ALT, generate=CFA.Model.NULL)
Output.ALT.ALT2 < sim(NULL, n=seq(50, 500, 5), model=CFA.Model.ALT, generate=CFA.Model.ALT)
# Plot logistic line for the power based on the derived cutoff from the null model
# along sample size values
plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, nullNested=Output.NULL.NULL2,
nullParent=Output.NULL.ALT2)
# Plot scatterplot for the power based on the derived cutoff from the null model
# along sample size values
plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, nullNested=Output.NULL.NULL2,
nullParent=Output.NULL.ALT2, logistic=FALSE)
# Plot scatterplot for the power based on the advanced CFI value
plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, cutoff=c(CFI=0.1), logistic=FALSE)
## End(Not run)

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