simsem: plotPowerFitNested – R documentation

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plotPowerFitNested

Plot power of rejecting a nested model in a nested model comparison by each fit index

Description

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).

Usage

plotPowerFitNested(altNested, altParent, nullNested = NULL, 
nullParent = NULL, cutoff = NULL, usedFit = NULL, alpha = 0.05, 
contN = TRUE, contMCAR = TRUE, contMAR = TRUE, useContour = TRUE, 
logistic = TRUE)

Arguments

`altNested`	`SimResult` that saves the simulation result of the nested model when the nested model is `FALSE`.
`altParent`	`SimResult` that saves the simulation result of the parent model when the nested model is `FALSE`.
`nullNested`	`SimResult` that saves the simulation result of the nested model when the nested model is `TRUE`. This argument may not be specified if the `cutoff` is specified.
`nullParent`	`SimResult` that saves the simulation result of the parent model when the nested model is `TRUE`. This argument may not be specified if the `cutoff` is specified.
`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 `plotCutoff` function will provide 3D graph. Contour graph is a default. However, if this is specified as `FALSE`, perspective plot is used.
`logistic`	If `logistic` is `TRUE` and the varying parameter exists (e.g., sample size or percent missing), the plot based on logistic regression predicting the significance by the varying parameters is preferred. If `FALSE`, the overlaying scatterplot with a line of cutoff is plotted.

Value

NONE. Only plot the fit indices distributions.

Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com)

Examples

## Not run: 
# Null model: One-factor 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: Two-factor 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 fine-grained 
# 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)

simsem

SIMulated Structural Equation Modeling

v0.5-16

GPL (>= 2)

Authors

Sunthud Pornprasertmanit [aut], Patrick Miller [aut], Alexander Schoemann [aut] (<https://orcid.org/0000-0002-8479-8798>), Terrence D. Jorgensen [aut, cre] (<https://orcid.org/0000-0001-5111-6773>), Corbin Quick [ctb]

Initial release

2021-03-28