kde {ks} | R Documentation |
Kernel density estimate for 2- to 6-dimensional 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 (required for dimensions > 3) |
The kernel density estimate is computed exactly i.e. binning is not used.
If eval.points=NULL
(default) then the
density estimate is automatically computed over a grid whose
resolution is controlled by gridsize
(default is
100 in each co-ordinate direction).
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.
### bivariate example data(unicef) H.pi <- Hpi(unicef, nstage=1) fhat <- kde(unicef, H.pi) ### 4-variate example library(MASS) data(iris) ir <- iris[,1:4][iris[,5]=="setosa",] H.scv <- Hscv(ir) fhat <- kde(ir, H.scv, eval.points=ir)