Plot a confidence interval width of a target parameter
1  plotCIwidth(object, targetParam, assurance = 0.50, useContour = TRUE)

object 
The target ( 
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 
NONE. The plot the confidence interval width is provided.
Sunthud Pornprasertmanit (psunthud@gmail.com)
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, 127148.
SimResult
for simResult that used in this function.
getCIwidth
to get confidence interval widths
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40  ## 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 finegrained
# 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 finegrained 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)
## End(Not run)

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