plotCIwidth: Plot a confidence interval width of a target parameter

Description

Plot a confidence interval width of a target parameter

Usage

plotCIwidth(object, targetParam, assurance = 0.50, useContour = TRUE)

Arguments

object

The target ('>SimResult

targetParam

One or more target parameters to be plotted

assurance

The percentile of the resulting width. When assurance is 0.50, the median of the widths is provided. See Lai & Kelley (2011) for more details.

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.

Value

NONE. The plot the confidence interval width is provided.

References

Lai, K., & Kelley, K. (2011). Accuracy in parameter estimation for targeted effects in structural equation modeling: Sample size planning for narrow confidence intervals. Psychological Methods, 16, 127-148.

Examples

Run this code

# 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 widths of factor correlation
plotCIwidth(Output, "f1~~f2", assurance = 0.80)

# 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 widths along sample size value
plotCIwidth(Output2, "f1~~f2", assurance = 0.80)

# 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 widths at each level
# of sample size and percent missing completely at random
plotCIwidth(Output3, "f1~~f2", assurance = 0.80)
# }