MASS (version 7.3-47)

predict.qda:

Description

Classify multivariate observations in conjunction with qda

Usage

# S3 method for qda
predict(object, newdata, prior = object$prior,
        method = c("plug-in", "predictive", "debiased", "looCV"), …)

Arguments

object

object of class "qda"

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 qda object.

prior

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

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. With "looCV" the leave-one-out cross-validation fits to the original dataset are computed and returned.

…

arguments based from or to other methods

Value

a list with components

class

The MAP classification (a factor)

posterior

posterior probabilities for the classes

Details

This function is a method for the generic function predict() for class "qda". It can be invoked by calling predict(x) for an object x of the appropriate class, or directly by calling predict.qda(x) regardless of the class of the object. Missing values in newdata are handled by returning NA if the quadratic discriminants cannot be evaluated. If newdata is omitted and the na.action of the fit omitted cases, these will be omitted on the prediction.

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

qda, lda, predict.lda

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)))
zq <- qda(train, cl)
predict(zq, test)$class