lda
, and also project data onto the
linear discriminants.
predict.lda(object, newdata, prior = object$prior, dimen, method = c("plug-in", "predictive", "debiased"), ...)
"lda"
lda
object.
lda
.
min(p, ng-1)
, only the first
dimen
discriminant components are used
(except for
method="predictive"
), and only those dimensions are returned in
x
.
"plug-in"
(the default) the usual
unbiased parameter estimates are used and assumed to be correct. With
"debiased"
an unbiased estimator of the
log posterior probabilities is used, and with
"predictive"
the parameter estimates are
integrated out using a vague prior.
dimen
discriminant variables
This function is a method for the generic function
for classlda
. It can be invoked by calling
for an objectx
of the appropriate class, or directly by
calling
regardless of the class of the object.
Missing values in
newdata
are
handled by returning
NA
if the linear
discriminants cannot be evaluated. If
newdata
is omitted and the
na.action
of the fit omitted cases, these
will be omitted on the prediction.
This version centres the linear discriminants so that the weighted mean
(weighted by
prior
) of the group
centroids is at the origin.
tr <- sample(1:50, 25) train <- rbind(iris[tr,,1], iris[tr,,2], iris[tr,,3]) test <- rbind(iris[-tr,,1], iris[-tr,,2], iris[-tr,,3]) cl <- factor(c(rep("s", 25), rep("c", 25), rep("v", 25))) z <- lda(train, cl) predict(z, test)$class