predict.lda {MASS}  R Documentation 
Classify multivariate observations in conjunction with lda
, and also
project data onto the linear discriminants.
## S3 method for class 'lda': predict(object, newdata, prior = object$prior, dimen, method = c("plugin", "predictive", "debiased"), ...)
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 the lda 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 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 "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 
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.
a list with components
class 
The MAP classification (a factor) 
posterior 
posterior probabilities for the classes 
x 
the scores of test cases on up to dimen discriminant variables

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.
data(iris3) 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