residuals.rpart: Residuals From a Fitted Rpart Object

In henckr/distRforest: Distribution-based Random Forest

Description

Method for residuals for an rpart object.

Usage

## S3 method for class 'rpart'
residuals(object, type = c("usual", "pearson", "deviance"), ...)

Arguments

object	fitted model object of class "rpart".
type	Indicates the type of residual desired. For regression or anova trees all three residual definitions reduce to y - fitted. This is the residual returned for user method trees as well. For classification trees the usual residuals are the misclassification losses L(actual, predicted) where L is the loss matrix. With default losses this residual is 0/1 for correct/incorrect classification. The pearson residual is (1-fitted)/sqrt(fitted(1-fitted)) and the deviance residual is sqrt(minus twice logarithm of fitted). For poisson and exp (or survival) trees, the usual residual is the observed - expected number of events. The pearson and deviance residuals are as defined in McCullagh and Nelder.
...	further arguments passed to or from other methods.

Value

Vector of residuals of type type from a fitted rpart object.

References

McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.

Examples

fit <- rpart(skips ~ Opening + Solder + Mask + PadType + Panel,
             data = solder, method = "anova")
summary(residuals(fit))
plot(predict(fit),residuals(fit))

henckr/distRforest documentation built on April 30, 2020, 8:10 p.m.