contour: Contours functions

Description

Contour levels and sizes.

Usage

contourLevels(x, ...)
## S3 method for class 'kde':
contourLevels(x, prob, cont, nlevels=5, approx=FALSE, ...)
## S3 method for class 'kda':
contourLevels(x, prob, cont, nlevels=5, approx=FALSE, ...)
contourSizes(x, abs.cont, cont=c(25,50,75), approx=FALSE)

Arguments

an object of class kde or kda

prob

vector of probabilities corresponding to highest density regions

cont

vector of percentages which correspond to the complement of prob

abs.cont

vector of absolute contour levels

nlevels

number of pretty contour levels

approx

flag to compute approximate contour levels. Default is FALSE.

...

other parameters

Value

--For contourLevels, for kde objects, returns vector of heights. For kda objects, returns a list of vectors, one for each training group.
--For contourSizes, an approximation of the Lebesgue measure of level set, i.e. length (d=1), area (d=2), volume (d=3), hyper-volume (d>4).

Details

--For contourLevels, the most straightforward is to specify prob. Heights of the corresponding highest density region with probability prob are computed. The cont parameter here is consistent with cont parameter from plot.kde and plot.kda i.e. cont=(1-prob)*100%. If both prob and cont are missing then a pretty set of nlevels contours are computed. --For contourSizes, the approximate Lebesgue measures are approximated by Riemann sums. Thsese are rough approximations and depend highly on the estimation grid, and so should be interpreted carefully.

If approx=FALSE, then the exact KDE is computed. Otherwise it is interpolated from an existing KDE grid. This can dramatically reduce computation time for large data sets.

Examples

Run this code

set.seed(8192)
x <- rmvnorm.mixt(n=1000, mus=c(0,0), Sigmas=diag(2), props=1)
fhat <- kde(x=x)
contourLevels(fhat, cont=c(75, 50, 25), approx=TRUE)
contourSizes(fhat, cont=25, approx=TRUE) 
   ## compare to approx circle of radius=0.75 with area=1.77