simulateSampDist {DAAG} | R Documentation |
Simulated sampling distribution of mean or other statistic
Description
Simulates the sample distribution of the specified statistic,
for samples of the size(s) specified in numINsamp
.
Additionally a with replacement) sample is drawn from the
specified population.
Usage
simulateSampDist(rpop = rnorm, numsamp = 100, numINsamp = c(4, 16),
FUN = mean, seed=NULL
)
Arguments
rpop |
Either a function that generates random samples from the specified distribution, or a vector of values that define the population (i.e., an empirical distribution) |
numsamp |
Number of samples that should be taken. For close approximation of the asymptotic distribution (e.g., for the mean) this number should be large |
numINsamp |
Size(s) of each of the |
FUN |
Function to calculate the statistic whose sampling distribution is to be simulated |
seed |
Optional seed for random number generation |
Value
List, with elements values
, numINsamp
and FUN
values |
Matrix, with dimensions |
numINsamp |
Input value of |
numsamp |
Input value of |
Author(s)
John Maindonald
References
Maindonald, J.H. and Braun, W.J. (3rd edn, 2010) Data Analysis and Graphics Using R, 3rd edn, Sections 3.3 and 3.4
See Also
help(plotSampDist)
Examples
## By default, sample from normal population
simAvs <- simulateSampDist()
par(pty="s")
plotSampDist(simAvs)
## Sample from empirical distribution
simAvs <- simulateSampDist(rpop=rivers)
plotSampDist(simAvs)
## The function is currently defined as
function(rpop=rnorm, numsamp=100, numINsamp=c(4,16), FUN=mean,
seed=NULL){
if(!is.null(seed))set.seed(seed)
funtxt <- deparse(substitute(FUN))
nDists <- length(numINsamp)+1
values <- matrix(0, nrow=numsamp, ncol=nDists)
if(!is.function(rpop)) {
x <- rpop
rpop <- function(n)sample(x, n, replace=TRUE)
}
values[,1] <- rpop(numsamp)
for(j in 2:nDists){
n <- numINsamp[j-1]
for(i in 1:numsamp)values[i, j] <- FUN(rpop(n))
}
colnames(values) <- paste("Size", c(1, numINsamp))
invisible(list(values=values, numINsamp=numINsamp, FUN=funtxt))
}