# NOT RUN {
# Specify Sample Size by n
loading <- matrix(0, 6, 1)
loading[1:6, 1] <- NA
LY <- bind(loading, 0.7)
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, CFA.Model, n=seq(100, 200, 20), pmMCAR=c(0, 0.1, 0.2))
summary(Output)
# Find the coverage rates of all combinations of different sample size and percent MCAR missing
Ccover <- continuousCoverage(Output, contN = TRUE, contMCAR = TRUE)
Ccover
# Find the coverage rates of parameter estimates when sample size is 200
# and percent MCAR missing is 0.3
Ccover2 <- continuousCoverage(Output, coverValue=0, contN = TRUE, contMCAR = TRUE,
pred=list(N = 200, pmMCAR = 0.3))
Ccover2
# }
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