plotSampDist: Plot(s) of simulated sampling distributions

Description

Plots are based on the output from simulateSampDist(). By default, both density plots and normal probability plots are given, for a sample from the specified population and for samples of the relevant size(s)

Usage

plotSampDist(sampvalues, graph = c("density", "qq"), cex = 0.925,
             titletext = "Empirical sampling distributions of the",
             popsample=TRUE, ...)

Arguments

sampvalues

Object output from simulateSampDist()

graph

Either or both of "density" and "qq"

cex

Character size parameter, relative to default

titletext

Title for graph

popsample

If TRUE show distribution of random sample from population

...

Other graphics parameters

Value

Plots graph(s), as described above.

References

Maindonald, J.H. and Braun, W.J. (2nd edn, 2006) "Data Analysis and Graphics Using R", Section 4.1

Examples

Run this code

## 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(sampvalues, graph=c("density", "qq"), cex=0.925,
           titletext="Empirical sampling distributions of the",
           popsample=TRUE, ...){
    if(length(graph)==2)oldpar <- par(mfrow=c(1,2), mar=c(3.1,4.1,1.6,0.6),
               mgp=c(2.5, 0.75, 0), oma=c(0,0,1.5,0), cex=cex)
    values <- sampvalues$values
    numINsamp <- sampvalues$numINsamp
    funtxt <- sampvalues$FUN
    nDists <- length(numINsamp)+1
    nfirst <- 2
    legitems <- paste("Size", numINsamp)
    if(popsample){nfirst <- 1
                  legitems <- c("Size 1", legitems)
                }
    if(match("density", graph)){
      popdens <- density(values[,1], ...)
      avdens <- vector("list", length=nDists)
      maxht <- max(popdens$y)
      ## For each sample size specified in numINsamp, calculate mean
      ## (or other statistic specified by FUN) for numsamp samples
      for(j in nfirst:nDists){
        av <- values[, j]
        avdens[[j]] <- density(av, ...)
        maxht <- max(maxht, avdens[[j]]$y)
      }
    }
    if(length(graph)>0)
      for(graphtype in graph){
        if(graphtype=="density"){
          if(popsample)
          plot(popdens, ylim=c(0, 1.2*maxht), type="l", yaxs="i",
               main="")
          else plot(avdens[[2]], type="n", ylim=c(0, 1.2*maxht),
                    yaxs="i", main="")
          for(j in 2:nDists)lines(avdens[[j]], col=j)
          legend("topleft",
                 legend=legitems,
                 col=nfirst:nDists, lty=rep(1,nDists-nfirst+1), cex=cex)
        }
        if(graphtype=="qq"){
          if(popsample) qqnorm(values[,1], main="")
          else qqnorm(values[,2], type="n")
          for(j in 2:nDists){
            qqav <- qqnorm(values[, j], plot.it=FALSE)
            points(qqav, col=j, pch=j)
           }
            legend("topleft", legend=legitems,
                   col=nfirst:nDists, pch=nfirst:nDists, cex=cex)
       }
      }
    if(par()$oma[3]>0){
      outer <- TRUE
      line=0
    }  else
    {
      outer <- FALSE
      line <- 1.25
    }
    if(!is.null(titletext))
      mtext(side=3, line=line,
            paste(titletext, funtxt),
            cex=1.1, outer=outer)
    if(length(graph)>1)par(oldpar)
  }