kda, Hkda, Hkda.diag, hkda {ks} | R Documentation |
Kernel discriminant analysis for 1- to 6-dimensional data.
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)
x |
matrix of training data values |
x.group |
vector of group labels for training data |
y |
matrix of test data |
Hs |
(stacked) matrix of bandwidth matrices |
hs |
vector of scalar bandwidths |
prior.prob |
vector of prior probabilities |
bw |
bandwidth: "plugin" = plug-in, "lscv" = LSCV,
"scv" = SCV |
nstage |
number of stages in the plug-in bandwidth selector (1 or 2) |
pilot |
"amse" = AMSE pilot bandwidths,
"samse" = single SAMSE pilot bandwidth |
pre |
"scale" = pre-scaling, "sphere" =
pre-sphering |
Hstart |
(stacked) matrix of initial bandwidth matrices, used in numerical optimisation |
binned |
flag for binned kernel estimation |
bgridsize |
vector of binning grid sizes -
required only if binned=TRUE |
– The values that valid for 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.
– The result from 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.
Mardia, K.V., Kent, J.T. & Bibby J.M. (1979) Multivariate Analysis. Academic Press. London.
Silverman, B. W. (1986) Data Analysis for Statistics and Data Analysis. Chapman & Hall. London.
Simonoff, J. S. (1996) Smoothing Methods in Statistics. Springer-Verlag. New York
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