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$prior, dimen, method = c("plug-in", "predictive", "debiased"), ...)
object |
object of class |
newdata |
data frame of cases to be classified or, if |
prior |
The prior probabilities of the classes, by default the proportions in the
training set or what was set in the call to |
dimen |
the dimension of the space to be used. If this is less than |
method |
This determines how the parameter estimation is handled. With |
... |
arguments based from or to other methods |
This function is a method for the generic function predict()
for
class "lda"
. It can be invoked by calling predict(x)
for
an object x
of the appropriate class, or directly by calling
predict.lda(x)
regardless of the class of the object.
Missing values in newdata
are handled by returning NA
if the
linear discriminants cannot be evaluated. If newdata
is omitted and
the na.action
of the fit omitted cases, these will be omitted on the
prediction.
This version centres the linear discriminants so that the
weighted mean (weighted by prior
) of the group centroids is at
the origin.
a list with components
class |
The MAP classification (a factor) |
posterior |
posterior probabilities for the classes |
x |
the scores of test cases on up to |
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
tr <- sample(1:50, 25) train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]) test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3]) cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) z <- lda(train, cl) predict(z, test)$class