predict.ranger: Ranger prediction

Description

Prediction with new data and a saved forest from Ranger.

Usage

# S3 method for ranger
predict(object, data, predict.all = FALSE,
  num.trees = object$num.trees, type = "response", seed = NULL,
  num.threads = NULL, verbose = TRUE, ...)

Arguments

object

Ranger ranger object.

data

New test data of class data.frame or gwaa.data (GenABEL).

predict.all

Return individual predictions for each tree instead of aggregated predictions for all trees. Return a matrix (sample x tree) for classification and regression, a 3d array for probability estimation (sample x class x tree) and survival (sample x time x tree).

num.trees

Number of trees used for prediction. The first num.trees in the forest are used.

type

Type of prediction. One of 'response', 'se', 'terminalNodes' with default 'response'. See below for details.

seed

Random seed used in Ranger.

num.threads

Number of threads. Default is number of CPUs available.

verbose

Verbose output on or off.

...

further arguments passed to or from other methods.

Value

Object of class ranger.prediction with elements

`predictions`	Predicted classes/values (only for classification and regression)
`unique.death.times`	Unique death times (only for survival).
`chf`	Estimated cumulative hazard function for each sample (only for survival).
`survival`	Estimated survival function for each sample (only for survival).
`num.trees`	Number of trees.
`num.independent.variables`	Number of independent variables.
`treetype`	Type of forest/tree. Classification, regression or survival.

Details

For type = 'response' (the default), the predicted classes (classification), predicted numeric values (regression), predicted probabilities (probability estimation) or survival probabilities (survival) are returned. For type = 'se', the standard error of the predictions are returned (regression only). The jackknife-after-bootstrap is used to estimate the standard errors based on out-of-bag predictions. See Wager et al. (2014) for details. For type = 'terminalNodes', the IDs of the terminal node in each tree for each observation in the given dataset are returned.

For classification and predict.all = TRUE, a factor levels are returned as numerics. To retrieve the corresponding factor levels, use rf$forest$levels, if rf is the ranger object.

References