contour {ks} | R Documentation |
Contour levels and sizes.
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)
x |
an 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 FALSE. |
... |
other parameters |
–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.
–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).
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, vol=pi*0.75^2=1.77