plotCutoff {simsem} | R Documentation |
Plot sampling distributions of fit indices with fit indices cutoffs
Description
This function will plot sampling distributions of fit indices. The users may add cutoffs by specifying the alpha
level.
Usage
plotCutoff(object, alpha = NULL, revDirec = FALSE, usedFit = NULL,
useContour = TRUE)
Arguments
object |
The target ( |
alpha |
A priori alpha level to get the cutoffs of fit indices |
revDirec |
The default is to find critical point on the side that indicates worse fit (the right side of RMSEA or the left side of CFI). If specifying as |
usedFit |
The name of fit indices that researchers wish to plot |
useContour |
If there are two things from varying sample size, varying percent completely at random, or varying percent missing at random, the |
Value
NONE. The plot the fit indices distributions is provided.
Author(s)
Sunthud Pornprasertmanit (psunthud@gmail.com)
See Also
-
SimResult
for simResult that used in this function. -
getCutoff
to find values of cutoffs based on null hypothesis sampling distributions only
Examples
## Not run:
loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
loadingValues <- matrix(0, 6, 2)
loadingValues[1:3, 1] <- 0.7
loadingValues[4:6, 2] <- 0.7
LY <- bind(loading, loadingValues)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPS <- binds(latent.cor, 0.5)
error.cor <- matrix(0, 6, 6)
diag(error.cor) <- 1
RTE <- binds(error.cor)
CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType="CFA")
# We make the examples running only 5 replications to save time.
# In reality, more replications are needed.
Output <- sim(5, n=200, model=CFA.Model)
# Plot the cutoffs with desired fit indices
plotCutoff(Output, 0.05, usedFit=c("RMSEA", "SRMR", "CFI", "TLI"))
# The example of continous varying sample size. Note that more fine-grained
# values of n is needed, e.g., n=seq(50, 500, 1)
Output2 <- sim(NULL, n=seq(450, 500, 10), model=CFA.Model)
# Plot the cutoffs along sample size value
plotCutoff(Output2, 0.05)
# Specify both continuous sample size and percent missing completely at random.
# Note that more fine-grained values of n and pmMCAR is needed, e.g., n=seq(50, 500, 1)
# and pmMCAR=seq(0, 0.2, 0.01)
Output3 <- sim(NULL, n=seq(450, 500, 10), pmMCAR=c(0, 0.05, 0.1, 0.15), model=CFA.Model)
# Plot the contours that each contour represents the value of cutoff at each level
# of sample size and percent missing completely at random
plotCutoff(Output3, 0.05)
## End(Not run)