predict.majorityGenerative: Classify Observations with the Weighted Majority Classifier

In schiffner/locClass: Collection of Local Classification Methods

Description

Classify observations in conjunction with majorityGenerative.

Usage

predict.majorityGenerative(object, newdata, prior = object$prior, ...)

Arguments

object	Object of class "majorityGenerative".
newdata	A 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 majorityGenerative object.
prior	The class prior probabilities. By default the proportions in the training data set.
...	Further arguments.

Details

This function is a method for the generic function predict() for class "majorityGenerative". It can be invoked by calling predict(x) for an object x of the appropriate class, or directly by calling predict.majorityGenerative(x) regardless of the class of the object.

Value

A list with components:

class	The predicted class labels (a factor).
posterior	Matrix of class posterior probabilities.

See Also

Other majority: majorityGenerative, majority, predict.majority

Examples

library(mlbench)
data(PimaIndiansDiabetes)

train <- sample(nrow(PimaIndiansDiabetes), 500)

# weighting observations from classes pos and neg according to their
# frequency in the data set:
ws <- as.numeric(1/table(PimaIndiansDiabetes$diabetes)
    [PimaIndiansDiabetes$diabetes])

fit <- majorityGenerative(diabetes ~ ., data = PimaIndiansDiabetes, weights = ws,
    subset = train)
pred <- predict(fit, newdata = PimaIndiansDiabetes[-train,])
mean(pred$class != PimaIndiansDiabetes$diabetes[-train])

schiffner/locClass documentation built on May 29, 2019, 3:39 p.m.