kda, pda {ks} | R Documentation |
Kernel and parametric discriminant analysis.
kda(x, x.group, Hs, y, prior.prob=NULL) pda(x, x.group, y, prior.prob=NULL, type="quad")
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 |
prior.prob |
vector of prior probabilities |
type |
"line" = linear discriminant, "quad" =
quadratic discriminant |
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 linear and quadratic discriminant analysers are based on
lda
and qda
from the MASS
library.
A vector of group labels estimated via a discriminant (or classification)
rule. If the test data y
are given then these are
classified. Otherwise the training data x
are classified.
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.
kda.kde
, pda.pde
,
compare
,
compare.kda.cv
,
compare.pda.cv
### bivariate example - restricted iris dataset library(MASS) data(iris) ir <- iris[,1:2] ir.gr <- iris[,5] H <- Hkda(ir, ir.gr, bw="plugin", pre="scale") kda.gr <- kda(ir, ir.gr, H, ir) lda.gr <- pda(ir, ir.gr, ir, type="line") qda.gr <- pda(ir, ir.gr, ir, type="quad") ## Not run: ### multivariate example - full iris dataset ir <- iris[,1:4] ir.gr <- iris[,5] H <- Hkda(ir, ir.gr, bw="plugin", pre="scale") kda.gr <- kda(ir, ir.gr, H, ir) lda.gr <- pda(ir, ir.gr, ir, type="line") qda.gr <- pda(ir, ir.gr, ir, type="quad") ## End(Not run)