Predictions from an Inbagg Object

Predicts the class membership of new observations through indirect bagging.

Usage

# S3 method for class 'inbagg'
predict(object, newdata, ...)

Arguments

object

object of class inbagg, see inbagg.

newdata

data frame to be classified.

...

additional argumends corresponding to the predictive models.

Details

Predictions of class memberships are calculated. i.e. values of the intermediate variables are predicted following pFUN and classified following cFUN, see inbagg.

Value

The vector of predicted classes is returned.

References

David J. Hand, Hua Gui Li, Niall M. Adams (2001), Supervised classification with structured class definitions. Computational Statistics & Data Analysis 36, 209–225.

Andrea Peters, Berthold Lausen, Georg Michelson and Olaf Gefeller (2003), Diagnosis of glaucoma by indirect classifiers. Methods of Information in Medicine 1, 99-103.

See also

inbagg

Examples

library("MASS")
library("rpart")
y <- as.factor(sample(1:2, 100, replace = TRUE))
W <- mvrnorm(n = 200, mu = rep(0, 3), Sigma = diag(3)) 
X <- mvrnorm(n = 200, mu = rep(2, 3), Sigma = diag(3))
colnames(W) <- c("w1", "w2", "w3")
colnames(X) <- c("x1", "x2", "x3")
DATA <- data.frame(y, W, X)

pFUN <- list(list(formula = w1~x1+x2, model = lm),
list(model = rpart))

RES <- inbagg(y~w1+w2+w3~x1+x2+x3, data = DATA, pFUN = pFUN)
predict(RES, newdata = X)
#>   [1] 1 2 1 1 2 1 1 1 1 1 1 1 1 2 1 1 2 1 2 2 2 1 2 1 2 2 1 1 1 1 1 1 2 1 2 2 2
#>  [38] 2 1 1 2 2 1 2 2 2 2 1 1 2 2 2 1 1 2 2 1 2 2 1 1 2 2 2 2 2 2 1 2 2 1 2 2 2
#>  [75] 2 1 2 2 1 2 1 1 2 2 1 1 2 1 1 2 2 1 1 2 1 1 2 1 1 1 1 2 2 1 2 1 1 1 1 1 1
#> [112] 1 1 2 1 1 2 1 2 2 2 1 2 1 2 2 1 1 1 1 1 1 2 1 2 2 2 2 1 1 2 2 1 2 2 2 1 1
#> [149] 1 2 2 2 1 1 2 2 1 2 2 1 1 2 2 2 2 2 2 1 2 2 1 2 2 2 2 1 2 2 1 2 1 1 2 2 1
#> [186] 1 2 1 1 2 1 1 1 2 1 1 2 1 1 1
#> Levels: 1 2