# 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)
# }
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