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