kde {ks} | R Documentation |
Kernel density estimate for 1- to 6-dimensional data.
kde(x, H, h, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points, binned=FALSE, bgridsize, positive=FALSE, adj.positive)
x |
matrix of data values |
H |
bandwidth matrix |
h |
scalar bandwidth |
gridsize |
vector of number of grid points |
gridtype |
not yet implemented |
xmin |
vector of minimum values for grid |
xmax |
vector of maximum values for grid |
supp |
effective support for standard normal is [-supp, supp ] |
eval.points |
points at which density estimate is evaluated |
binned |
flag for binned estimation (default is FALSE) |
bgridsize |
vector of binning grid sizes - required if binned=TRUE |
positive |
flag if 1-d data are positive (default is FALSE) |
adj.positive |
adjustment added to data i.e. when
positive=TRUE KDE is carried out on log(x +
adj.positive) . Default is the minimum of x . |
For d = 1, 2, 3, 4,
and if eval.points
is not specified, then the
density estimate is computed over a grid
defined by gridsize
(if binned=FALSE
) or
by bgridsize
(if binned=TRUE
).
For d = 1, 2, 3, 4,
and if eval.points
is specified, then the
density estimate is computed exactly at eval.points
.
For d > 4, the kernel density estimate is computed exactly
and eval.points
must be specified.
The default xmin
is min(x) - Hmax*supp
and xmax
is max(x) + Hmax*supp
where Hmax
is the maximim of the
diagonal elements of H
.
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 at which the density estimate is evaluated |
estimate |
density estimate at eval.points |
H |
bandwidth matrix |
h |
scalar bandwidth (1-d only) |
Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall. London.
### See examples in ? plot.kde