predict.inbagg {ipred} | R Documentation |

## Predictions from an Inbagg Object

### Description

Predicts the class membership of new observations through indirect
bagging.

### Usage

predict.inbagg(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.

### Author(s)

Andrea Peters <Peters.Andrea@imbe.imed.uni-erlangen.de>

### 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(mvtnorm)
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)

[Package

*ipred* version 0.8-1

Index]