Plot a confidence interval width of a target parameter
Plot a confidence interval width of a target parameter
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, 127-148.
SimResult for simResult that used in this function.
getCIwidth to get confidence interval widths
## 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)
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