predict.lda(object, newdata, dimen, method = c("plug-in", "predictive", "debiased"))
object
|
object of class lda
|
newdata
|
matrix or data frame of cases to be classified or, if object
has a formula, a data frame with columns of the same names as the
variables used. A vector will be interpreted
as a row vector. If newdata is missing, an attempt will be
made to retrieve the data used to fit the lda object.
|
dimen
|
the dimension of the space to be used. If this is less than min(p, ng-1) ,
only the first dimen discriminant components are used (except for
method="predictive" ), and only those dimensions are returned in x .
|
method
|
This determines how the parameter estimation is handled. With "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.
|
lda
, and also
project data onto the linear discriminants.predict()
for class lda
.
It can be invoked by calling predict(x)
for an
object x
of the appropriate class, or directly by
calling predict.lda(x)
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.
class
| The MAP classification (a factor) |
posterior
| posterior probabilities for the classes |
x
|
the scores of test cases on up to dimen discriminant variables
|
lda
, qda
, predict.qda
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