plotCIwidth {simsem} | R Documentation |
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 ( |
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 |
Value
NONE. The plot the confidence interval width is provided.
Author(s)
Sunthud Pornprasertmanit (psunthud@gmail.com)
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.
See Also
-
SimResult
for simResult that used in this function. -
getCIwidth
to get confidence interval widths
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 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)
## End(Not run)