ranger: predict.ranger.forest – R documentation

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ranger

predict.ranger.forest

Ranger prediction

Description

Prediction with new data and a saved forest from Ranger.

Usage

## S3 method for class 'ranger.forest'
predict(
  object,
  data,
  predict.all = FALSE,
  num.trees = object$num.trees,
  type = "response",
  se.method = "infjack",
  seed = NULL,
  num.threads = NULL,
  verbose = TRUE,
  inbag.counts = NULL,
  ...
)

Arguments

`object`	Ranger `ranger.forest` 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', 'quantiles' with default 'response'. See below for details.
`se.method`	Method to compute standard errors. One of 'jack', 'infjack' with default 'infjack'. Only applicable if type = 'se'. See below for details.
`seed`	Random seed. Default is `NULL`, which generates the seed from `R`. Set to `0` to ignore the `R` seed. The seed is used in case of ties in classification mode.
`num.threads`	Number of threads. Default is number of CPUs available.
`verbose`	Verbose output on or off.
`inbag.counts`	Number of times the observations are in-bag in the trees.
`...`	further arguments passed to or from other methods.

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 or infinitesimal jackknife for bagging 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.

If type = 'se' is selected, the method to estimate the variances can be chosen with se.method. Set se.method = 'jack' for jackknife after bootstrap and se.method = 'infjack' for the infinitesimal jackknife for bagging.

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.

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.
`num.samples`	Number of samples.

Author(s)

Marvin N. Wright

References

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. https://doi.org/10.18637/jss.v077.i01.
Wager, S., Hastie T., & Efron, B. (2014). Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife. J Mach Learn Res 15:1625-1651. http://jmlr.org/papers/v15/wager14a.html.