predict.lda
Classify multivariate observations in conjunction with lda
, and also
project data onto the linear discriminants.
 Keywords
 multivariate
Usage
# S3 method for lda
predict(object, newdata, prior = object$prior, dimen,
method = c("plugin", "predictive", "debiased"), …)
Arguments
 object

object of class
"lda"
 newdata

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 thelda
object.  prior

The prior probabilities of the classes, by default the proportions in the
training set or what was set in the call to
lda
.  dimen

the dimension of the space to be used. If this is less than
min(p, ng1)
, only the firstdimen
discriminant components are used (except formethod="predictive"
), and only those dimensions are returned inx
.  method

This determines how the parameter estimation is handled. With
"plugin"
(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.  …
 arguments based from or to other methods
Details
This function is a method for the generic function 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. This version centres the linear discriminants so that the
weighted mean (weighted by prior
) of the group centroids is at
the origin.
Value
a list with components
dimen
discriminant variables
References
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
See Also
Examples
library(MASS)
tr < sample(1:50, 25)
train < rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
test < rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
cl < factor(c(rep("s",25), rep("c",25), rep("v",25)))
z < lda(train, cl)
predict(z, test)$class