Predictions from an Inbagg Object
Predicts the class membership of new observations through indirect bagging.
## S3 method for class 'inbagg' predict(object, newdata, ...)
object |
object of class |
newdata |
data frame to be classified. |
... |
additional argumends corresponding to the predictive models. |
Predictions of class memberships are calculated. i.e. values of the
intermediate variables are predicted following pFUN and classified following cFUN,
see inbagg.
The vector of predicted classes is returned.
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.