ranger (version 0.12.1)

predict.ranger: Ranger prediction

Description

Prediction with new data and a saved forest from Ranger.

Usage

# S3 method for ranger
predict(
  object,
  data = NULL,
  predict.all = FALSE,
  num.trees = object$num.trees,
  type = "response",
  se.method = "infjack",
  quantiles = c(0.1, 0.5, 0.9),
  what = NULL,
  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', '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.

quantiles

Vector of quantiles for quantile prediction. Set type = 'quantiles' to use.

what

User specified function for quantile prediction used instead of quantile. Must return numeric vector, see examples.

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.

...

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 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. For type = 'quantiles', the selected quantiles for each observation are estimated. See Meinshausen (2006) for details.

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.

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.

• Meinshausen (2006). Quantile Regression Forests. J Mach Learn Res 7:983-999. http://www.jmlr.org/papers/v7/meinshausen06a.html.

See Also

ranger

Examples

# NOT RUN {
## Classification forest
ranger(Species ~ ., data = iris)
train.idx <- sample(nrow(iris), 2/3 * nrow(iris))
iris.train <- iris[train.idx, ]
iris.test <- iris[-train.idx, ]
rg.iris <- ranger(Species ~ ., data = iris.train)
pred.iris <- predict(rg.iris, data = iris.test)
table(iris.test$Species, pred.iris$predictions)

## Quantile regression forest
rf <- ranger(mpg ~ ., mtcars[1:26, ], quantreg = TRUE)
pred <- predict(rf, mtcars[27:32, ], type = "quantiles", quantiles = c(0.1, 0.5, 0.9))
pred$predictions

## Quantile regression forest with user-specified function
rf <- ranger(mpg ~ ., mtcars[1:26, ], quantreg = TRUE)
pred <- predict(rf, mtcars[27:32, ], type = "quantiles", 
                what = function(x) sample(x, 10, replace = TRUE))
pred$predictions

# }