DAAG: simulateSampDist – R documentation

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simulateSampDist

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 `numsamp` sample(s)
`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 `numsamp` by `numINsamp + 1`. The first column has a random with replacement sample from the population, while the remaining `length(numINsamp)` columns hold simulated values from sampling distributions with samples of the specified size(s)
`numINsamp`	Input value of `numINsamp`
`numsamp`	Input value of `numsamp`

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

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))
  }

DAAG

Data Analysis and Graphics Data and Functions

v1.24

GPL-3

Authors

John H. Maindonald and W. John Braun

Initial release