(This article was first published on

**Quantitative Ecology**, and kindly contributed to R-bloggers)

Plenty of packages allow you to plot contours of a “z” value; however, I wanted to be able to plot a specific density contour of a sample from a bivariate distribution over a plot that was a function of the x and y parameters. The example only plots the density of x and y; however, if you set plot.dens=FALSE, the contours will be added to the active display device (i.e., over an image(x,y,function(x,y){…})).

This function also lets you specify the line widths and types for each of the contours.

###########################################

## drawcontour.R

## Written by J.D. Forester, 17 March 2008

###########################################

##This function draws an approximate density contour based on

##empirical, bivariate data.

##change testit to FALSE if sourcing the file

testit=TRUE

draw.contour<-function(a,alpha=0.95,plot.dens=FALSE, line.width=2, line.type=1, limits=NULL, density.res=300,spline.smooth=-1,...){

##a is a list or matrix of x and y coordinates (e.g., a=list("x"=rnorm(100),"y"=rnorm(100)))

## if a is a list or dataframe, the components must be labeled "x" and "y"

## if a is a matrix, the first column is assumed to be x, the second y

##alpha is the contour level desired

##if plot.dens==TRUE, then the joint density of x and y are plotted,

## otherwise the contour is added to the current plot.

##density.res controls the resolution of the density plot

##A key assumption of this function is that very little probability mass lies outside the limits of

## the x and y values in "a". This is likely reasonable if the number of observations in a is large.

require(MASS)

require(ks)

if(length(line.width)!=length(alpha)){

line.width <- rep(line.width[1],length(alpha))

}

if(length(line.type)!=length(alpha)){

line.type <- rep(line.type[1],length(alpha))

}

if(is.matrix(a)){

a=list("x"=a[,1],"y"=a[,2])

}

##generate approximate density values

if(is.null(limits)){

limits=c(range(a$x),range(a$y))

}

f1<-kde2d(a$x,a$y,n=density.res,lims=limits)

##plot empirical density

if(plot.dens) image(f1,...)

if(is.null(dev.list())){

##ensure that there is a window in which to draw the contour

plot(a,type="n",xlim=limits[1:2],ylim=limits[3:4],...)

}

##estimate critical contour value

## assume that density outside of plot is very small

zdens <- rev(sort(f1$z))

Czdens <- cumsum(zdens)

Czdens <- (Czdens/Czdens[length(zdens)])

for(cont.level in 1:length(alpha)){

##This loop allows for multiple contour levels

crit.val <- zdens[max(which(Czdens<=alpha[cont.level]))]

##determine coordinates of critical contour

b.full=contourLines(f1,levels=crit.val)

for(c in 1:length(b.full)){

##This loop is used in case the density is multimodal or if the desired contour

## extends outside the plotting region

b=list("x"=as.vector(unlist(b.full[c][2])),"y"=as.vector(unlist(b.full[c][3])))

##plot desired contour

line.dat<-xspline(b,shape=spline.smooth,open=TRUE,draw=FALSE)

lines(line.dat,lty=line.type[cont.level],lwd=line.width[cont.level])

}

}

}

##############################

##Test the function

##############################

##generate data

if(testit){

n=10000

a<-list("x"=rnorm(n,400,100),"y"=rweibull(n,2,100))

draw.contour(a=a,alpha=c(0.95,0.5,0.05),line.width=c(2,1,2),line.type=c(1,2,1),plot.dens=TRUE, xlab="X", ylab="Y")

}

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