| kda.kde {ks} | R Documentation |
Kernel density estimate for kernel discriminant analysis for 1- to 6-dimensional data
kda.kde(x, x.group, Hs, hs, prior.prob=NULL, gridsize, xmin, xmax,
supp=3.7, eval.points=NULL, binned=FALSE, bgridsize)
x |
matrix of training data values |
x.group |
vector of group labels for training data |
Hs |
(stacked) matrix of bandwidth matrices |
hs |
vector of scalar bandwidths |
prior.prob |
vector of prior probabilities |
gridsize |
vector of number of grid points |
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 kernel estimation |
bgridsize |
vector of binning grid sizes - only required if
binned=TRUE |
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 is computed exactly at eval.points.
For d > 4, the kernel density estimate is computed exactly
and eval.points must be specified.
If you have prior probabilities then set prior.prob to these.
Otherwise prior.prob=NULL is the default i.e. use the sample
proportions as estimates of the prior probabilities.
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.
The kernel density estimate for kernel discriminant analysis is
based on kde, one density estimate for each group.
The result from kda.kde is a density estimate
for discriminant analysis is an object of class kda.kde which is a
list with 6 fields
x |
data points - same as input |
x.group |
group labels - same as input |
eval.points |
points that density estimate is evaluated at |
estimate |
density estimate at eval.points |
prior.prob |
prior probabilities |
H |
bandwidth matrices (>1-d only) or |
h |
bandwidths (1-d only) |
Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall. London.
### See examples in ? plot.kda.kde