# Math 322 Biostatistics
# Lecture, February 2, 2009
# R functions used for examples from Chapter 6.5-6.7 (Estimation)
# Geir Arne Hjelle (hjelle@math.wustl.edu)

generate.samples <- function(pop, n, samsize = 10, ...) {
  sams <- matrix(nrow = samsize, ncol = n)
  for (i in 1:n)
    sams[,i] <- sample(pop, samsize)

  return(sams)
}

estimate.mean <- function(pop, n, samsize = 10, ...) {
  sams <- generate.samples(pop, n, samsize)
  means <- colMeans(sams)

  hist(means, ...)

  return( sd(means) )
}

estimate.median <- function(pop, n, samsize = 10, ...) {
  sams <- generate.samples(pop, n, samsize)
  medians <- vector()
  for (i in 1:n)
    medians[i] <- median(sams[,i])

  hist(medians, ...)

  return( sd(medians) )
}

estimate.minmax <- function(pop, n, samsize = 10, ...) {
  sams <- generate.samples(pop, n, samsize)
  minmaxs <- vector()
  for (i in 1:n)
    minmaxs[i] <- mean(range(sams[,i]))

  hist(minmaxs, ...)

  return( sd(minmaxs) )
}

estimate.max <- function(pop, n, samsize = 10, ...) {
  sams <- generate.samples(pop, n, samsize)
  maxs <- vector()
  for (i in 1:n)
    maxs[i] <- max(sams[,i])

  hist(maxs, ...)

  return( sd(maxs) )
}