predict.wlda: Classify Observations Based on Weighted Linear Discriminant...

Description Usage Arguments Details Value See Also Examples

View source: R/wlda.R

Description

Classify observations in conjunction with wlda.

Usage

1
predict.wlda(object, newdata, prior = object$prior, ...)

Arguments

object

Object of class "wlda".

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 wlda 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 "wlda". It can be invoked by calling predict(x) for an object x of the appropriate class, or directly by calling predict.wlda(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 lda: wlda

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
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 <- wlda(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.