contour | R Documentation |
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)
contourProbs(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. and for contourProbs
,
the probability, 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).
–For contourProbs
, returns an approximation of the probability measure of
level set.
contour
, contourLines
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))
contourProbs(fhat, abs.cont=contourLevels(fhat, cont=50))
## compare approx prob with target prob=0.5
contourSizes(fhat, cont=25, approx=TRUE)
## compare to approx circle of radius=0.75 with area=1.77
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