Plot sampling distributions of fit indices that visualize power of rejecting datasets underlying misspecified models
This function will plot sampling distributions of fit indices that visualize power in rejecting the misspecified models
plotPowerFit(altObject, nullObject = NULL, cutoff = NULL, usedFit = NULL, alpha = 0.05, contN = TRUE, contMCAR = TRUE, contMAR = TRUE, useContour = TRUE, logistic = TRUE)
altObject |
The result object ( |
nullObject |
The result object ( |
cutoff |
A vector of priori cutoffs for 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.
getCutoff to find values of cutoffs based on null hypothesis sampling distributions only
getPowerFit to find power of rejecting the hypothesized model when the hypothesized model is FALSE.
## 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")
# We make the examples running only 5 replications to save time.
# In reality, more replications are needed.
Output.NULL <- sim(50, n=50, model=CFA.Model.NULL, generate=CFA.Model.NULL)
# 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, 0.5)
CFA.Model.ALT <- model(LY = LY.ALT, RPS = RPS.ALT, RTE = RTE, modelType="CFA")
Output.ALT <- sim(50, n=50, model=CFA.Model.NULL, generate=CFA.Model.ALT)
# Plot the power based on derived cutoff from the null model using four fit indices
plotPowerFit(Output.ALT, nullObject=Output.NULL, alpha=0.05,
usedFit=c("RMSEA", "CFI", "TLI", "SRMR"))
# Plot the power of rejecting null model when the rule of thumb from Hu & Bentler (1999) is used
Rule.of.thumb <- c(RMSEA=0.05, CFI=0.95, TLI=0.95, SRMR=0.06)
plotPowerFit(Output.ALT, cutoff=Rule.of.thumb, alpha=0.05,
usedFit=c("RMSEA", "CFI", "TLI", "SRMR"))
# 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.NULL2 <- sim(NULL, n=seq(50, 250, 25), model=CFA.Model.NULL, generate=CFA.Model.NULL)
Output.ALT2 <- sim(NULL, n=seq(50, 250, 25), model=CFA.Model.NULL, generate=CFA.Model.ALT)
# Plot the power based on derived cutoff from the null model using four fit indices
# along sample size
plotPowerFit(Output.ALT2, nullObject=Output.NULL2, alpha=0.05,
usedFit=c("RMSEA", "CFI", "TLI", "SRMR"))
# Plot the power based on rule of thumb along sample size
plotPowerFit(Output.ALT2, cutoff=Rule.of.thumb, alpha=0.05,
usedFit=c("RMSEA", "CFI", "TLI", "SRMR"))
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.