Contour functions
Contour levels and sizes.
contourLevels(x, ...) ## S3 method for class 'kde' contourLevels(x, prob, cont, nlevels=5, approx=TRUE, ...) ## S3 method for class 'kda' contourLevels(x, prob, cont, nlevels=5, approx=TRUE, ...) ## S3 method for class 'kdde' contourLevels(x, prob, cont, nlevels=5, approx=TRUE, which.deriv.ind=1, ...) contourSizes(x, abs.cont, cont=c(25,50,75), approx=TRUE)
x |
object of class |
prob |
vector of probabilities corresponding to highest density regions |
cont |
vector of percentages which correspond to the complement
of |
abs.cont |
vector of absolute contour levels |
nlevels |
number of pretty contour levels |
approx |
flag to compute approximate contour levels. Default is TRUE. |
which.deriv.ind |
partial derivative index. Default is 1. |
... |
other parameters |
–For contourLevels, the most straightforward is to specify prob.
The heights of
the corresponding highest density region with probability prob are
computed. The cont parameter here is consistent with
cont parameter from plot.kde, plot.kdde, 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 length, area, volume etc. are
approximated by Riemann sums. These 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.
–For contourLevels, for kde objects, returns vector of
heights. For kda objects, returns a list of vectors, one for
each training group. For kdde objects, returns a matrix of
vectors, one row for each partial derivative.
–For contourSizes, returns an approximation of the Lebesgue measure of
level set, i.e. length (d=1), area (d=2), volume (d=3), hyper-volume (d>4).
set.seed(8192) x <- rmvnorm.mixt(n=1000, mus=c(0,0), Sigmas=diag(2), props=1) fhat <- kde(x=x, binned=TRUE) contourLevels(fhat, cont=c(75, 50, 25)) contourSizes(fhat, cont=25, approx=TRUE) ## compare to approx circle of radius=0.75 with area=1.77
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