Hkda(x, x.group, Hstart, bw="plugin", nstage=2, pilot="samse",
pre="sphere", binned=FALSE, bgridsize)
Hkda.diag(x, x.group, bw="plugin", nstage=2, pilot="samse",
pre="sphere", binned=FALSE, bgridsize)
hkda(x, x.group, bw="plugin", nstage=2, binned=TRUE, bgridsize)kda(x, x.group, Hs, hs, y, prior.prob=NULL)
Hkda and Hkda.diag is a stacked matrix
of bandwidth matrices, one for each training data group. The result
from hkda is a vector of bandwidths, one for each training data
group. -- The result from kda is a vector of group labels
estimated via the kernel discriminant rule. If the test data y are
given then these are classified. Otherwise the training data x
are classified.
bw are "plugin", "lscv" and
"scv" for
Hkda. These in turn call Hpi,
Hlscv and Hscv. For plugin selectors, all
of nstage, pilot and pre need to be set. For SCV
selectors, currently nstage=1 always but pilot and pre
need to be set. For LSCV selectors, none of them are required.
Hkda.diag makes analagous calls to diagonal selectors. 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.
For details on the pre-transformations in pre, see
pre.sphere and pre.scale.
-- 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.
Venables, W.N. & Ripley, B.D. (1997) Modern Applied Statistics with S-PLUS. Springer-Verlag. New York.
compare,
compare.kda.cv,
kda.kde### See examples in ? plot.kda.kde }
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