| contourLevels {ks} | R Documentation |
Contour levels for kde and kda.kde objects.
contourLevels(x, ...) ## S3 method for class 'kde': contourLevels(x, prob, cont, nlevels=5, ...) ## S3 method for class 'kda.kde': contourLevels(x, prob, cont, nlevels=5, ...)
x |
an object of class kde or kda.kde |
prob |
vector of probabilities corresponding to highest density regions |
cont |
vector of percentages which correspond to the complement
of prob |
nlevels |
number of pretty contour levels |
... |
other parameters for contour |
The most straightfoward 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.kde
i.e. cont = (1 - prob)*100%.
If both prob and cont are missing then a pretty set of
nlevels contours are computed.
For kde objects, returns vector of heights. For kda.kde
objects, returns a list of vectors, one for each training group.
## kde x <- rmvnorm.mixt(n=100, mus=c(0,0), Sigmas=diag(2), props=1) Hx <- Hpi(x) fhatx <- kde(x=x, H=Hx) lev1 <- contourLevels(fhatx, prob=c(0.25, 0.5, 0.75)) lev2 <- contourLevels(fhatx, cont=c(75, 50, 25)) ## lev1 = lev2 ## kda.kde library(MASS) data(iris) ir <- iris[,1] ir.gr <- iris[,5] kda.fhat <- kda.kde(ir, ir.gr, hs=sqrt(c(0.01, 0.04, 0.07))) contourLevels(kda.fhat, prob=c(0.25, 0.5, 0.75))