kde {ks} | R Documentation |
Kernel density estimate for bivariate data.
kde(x, H, gridsize, supp=3.7, eval.points)
x |
matrix of data values |
H |
bandwidth matrix |
gridsize |
vector of number of grid points |
supp |
effective support for standard normal is [-supp, supp ] |
eval.points |
points that density estimate is evaluated at |
The kernel density estimate is computed exactly i.e. binning is not used.
If gridsize
is not set to a specific value, then it
defaults to 50 grid points in each co-ordinate direction
i.e. c(50,50)
. Not required
to be set if specifying eval.points
.
If eval.points
is not specified, then the
density estimate is automatically computed over a grid whose
resolution is controlled by gridsize
(a grid is
required for plotting).
Kernel density estimate is an object of class kde
which is a
list with 4 fields
x |
data points - same as input |
eval.points |
points that density estimate is evaluated at |
estimate |
density estimate at eval.points |
H |
bandwidth matrix |
Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall. London.
data(unicef) H.pi <- Hpi(unicef, nstage=1) H.pi1 <- invvech(c(797.5755, -106.63338, 19.56761)) fhat <- kde(unicef, H.pi)