# from demo(UserGuide16)
data(height, package = "R2MLwiN")
summary(height)
hist(height$height)
1 - pnorm((200 - mean(height$height)) / sd(height$height))
heightsim1 <- function() {
heightsim <- 175.35 + 10.002 * qnorm(runif(100))
c(pmean = mean(heightsim), pvar = var(heightsim))
}
set.seed(1)
# Note: To obtain estimates as close as possible to the MLwiN manual,
# increase the number of reps to 10000.
simdata1 <- as.data.frame(t(replicate(1000, heightsim1())))
simdata1$iteration <- 1:nrow(simdata1)
plot(simdata1$iteration, simdata1$pmean, type = "l")
plot(density(simdata1$pmean))
quantile(simdata1$pmean, c(0.025, 0.975))
plot(simdata1$iteration, simdata1$pvar, type = "l")
plot(density(simdata1$pvar))
quantile(simdata1$pvar, c(0.025, 0.975))
heightsim2 <- function(variable) {
samp <- sample(variable, replace = TRUE)
c(npmean = mean(samp), npvar = var(samp))
}
simdata2 <- as.data.frame(t(replicate(1000, heightsim2(height$height))))
simdata2$iteration <- 1:nrow(simdata2)
plot(simdata2$iteration, simdata2$npmean, type = "l")
plot(density(simdata2$npmean))
quantile(simdata2$npmean, c(0.025, 0.975))
plot(simdata2$iteration, simdata2$npvar, type = "l")
plot(density(simdata2$npvar))
quantile(simdata2$npvar, c(0.025, 0.975))
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