# NOT RUN {
loading <- matrix(0, 6, 1)
loading[1:6, 1] <- NA
LY <- bind(loading, 0.4)
RPS <- binds(diag(1))
RTE <- binds(diag(6))
CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType="CFA")
# 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)
Output <- sim(NULL, n=seq(100, 200, 20), pmMCAR=c(0, 0.1, 0.2), model=CFA.Model)
# Plot the power of the first factor loading along the sample size value
plotPower(Output, "f1=~y1", contMCAR=FALSE)
# Plot the power of the correlation along the sample size and percent missing completely at random
plotPower(Output, "f1=~y1")
# }
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