sdmsim.R

sdm.sim <- function(n,src.dist="E",param1=NULL,param2=NULL) {
  r <- 10000  # Number of replications/samples - DO NOT ADJUST
  # This produces a matrix of observations with  
  # n columns and r rows. Each row is one sample:
  my.samples <- switch(src.dist,
	   "E" = matrix(rexp(n*r,param1),r),
	   "N" = matrix(rnorm(n*r,param1,param2),r),
	   "U" = matrix(runif(n*r,param1,param2),r),
	   "P" = matrix(rpois(n*r,param1),r),
     "B" = matrix(rbinom(n*r,param1,param2),r),
	   "G" = matrix(rgamma(n*r,param1,param2),r),
	   "X" = matrix(rchisq(n*r,param1),r),
	   "T" = matrix(rt(n*r,param1),r))
  all.sample.sums <- apply(my.samples,1,sum)
  all.sample.means <- apply(my.samples,1,mean)   
  all.sample.vars <- apply(my.samples,1,var) 
  par(mfrow=c(2,2))
  hist(my.samples[1,],col="gray",main="Distribution of One Sample")
  hist(all.sample.sums,col="gray",main="Sampling Distribution\nof the Sum")
  hist(all.sample.means,col="gray",main="Sampling Distribution\nof the Mean")
  hist(all.sample.vars,col="gray",main="Sampling Distribution\nof the Variance")
}