predict.lda
Classify Multivariate Observations by Linear Discrimination
Description
Classify multivariate observations in conjunction with lda
, and also project data onto the linear discriminants.
Usage
## S3 method for class 'lda' predict(object, newdata, prior = object$prior, dimen, method = c("plug-in", "predictive", "debiased"), ...)
Arguments
object | object of class |
newdata | data frame of cases to be classified or, if |
prior | The prior probabilities of the classes, by default the proportions in the training set or what was set in the call to |
dimen | the dimension of the space to be used. If this is less than |
method | This determines how the parameter estimation is handled. With |
... | arguments based from or to other methods |
Details
This function is a method for the generic function predict()
for class "lda"
. It can be invoked by calling predict(x)
for an object x
of the appropriate class, or directly by calling predict.lda(x)
regardless of the class of the object.
Missing values in newdata
are handled by returning NA
if the linear discriminants cannot be evaluated. If newdata
is omitted and the na.action
of the fit omitted cases, these will be omitted on the prediction.
This version centres the linear discriminants so that the weighted mean (weighted by prior
) of the group centroids is at the origin.
Value
a list with components
class | The MAP classification (a factor) |
posterior | posterior probabilities for the classes |
x | the scores of test cases on up to |
References
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
See Also
Examples
tr <- sample(1:50, 25) train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]) test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3]) cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) z <- lda(train, cl) predict(z, test)$class
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Licensed under the GNU General Public License.