# Plot(s) of simulated sampling distributions 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) ```r 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 ## Returns Plots graph(s), as described above. ## References Maindonald, J.H. and Braun, W.J. (3rd edn, 2010) Data Analysis and Graphics Using R , Sections 3.3 and 3.4. ## Author(s) John Maindonald ## See Also See Also `help(simulateSampDist)` ## Examples ```r ## 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) } ```

plotSampDist function