predict.rfCountData: predict method for random forest Count Data objects

In fpechon/rfCountData: Random Forests for Count Data

Description

Prediction of test data using random forest.

Usage

## S3 method for class 'rfCountData'
predict(object, newdata, offset, type = "response",
  norm.votes = TRUE, predict.all = FALSE, proximity = FALSE,
  nodes = FALSE, cutoff, ...)

Arguments

object	an object of class rfCountData, as that created by the function rf
newdata	a data frame or matrix containing new data. (Note: If not given, the out-of-bag prediction in object is returned.
offset	a vector with a length equal to the number of rows in newdata. Log of time of exposure.
type	To be removed, or score scale vs. response scale ?
norm.votes	not used
predict.all	Should the predictions of all trees be kept?
proximity	To be removed
nodes	Should the terminal node indicators (an n by ntree matrix) be return? If so, it is in the 'nodes' attribute of the returned object.
cutoff	To be removed
...	Currently not used

Details

predict.rfCountData

Value

A vector of predicted values is returned.
If predict.all=TRUE, then the returned object is a list of two components: aggregate, which is the vector of predicted values by the forest, and individual, which is a matrix where each column contains prediction by a tree in the forest.
If predict.all=TRUE, then the individual component of thereturned object is a character matrix where each column contains the predicted class by a tree in the forest.
If nodes=TRUE, the returned object has a 'nodes' attribute, which is an n by ntree matrix, each column containing the node number that the cases fall in for that tree.

Author(s)

Andy Liaw [e-mail] and Matthew Wiener [e-mail], based on original Fortran code by Leo Breiman and Adele Cutler.

References

Breiman, L. (2001), Random Forests, Machine Learning 45(1),5-32.

See Also

rfPoisson

fpechon/rfCountData documentation built on Aug. 12, 2019, 11:16 a.m.