plotCutoff: Plot sampling distributions of fit indices with fit indices...
In simsem: SIMulated Structural Equation Modeling

Description Usage Arguments Value Author(s) See Also Examples

This function will plot sampling distributions of fit indices. The users may add cutoffs by specifying the alpha level.

1 2	plotCutoff(object, alpha = NULL, revDirec = FALSE, usedFit = NULL, useContour = TRUE)

`object`	The target (`SimResult`
`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 `TRUE`, the directions are reversed.
`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 `plotCutoff` function will provide 3D graph. A contour graph is a default. However, if this is specified as `FALSE`, perspective plot is used.

NONE. The plot the fit indices distributions is provided.

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

## 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)