| Title: | Quantile Regression Forests for 'ranger' |
|---|---|
| Description: | This is the implementation of quantile regression forests for the fast random forest package 'ranger'. |
| Authors: | Philipp Probst [aut, cre] |
| Maintainer: | Philipp Probst <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0 |
| Built: | 2024-11-04 03:17:55 UTC |
| Source: | https://github.com/philipppro/quantregranger |
Predicts quantiles for a quantile regression forest trained with quantregRanger.
## S3 method for class 'quantregRanger' predict(object, data = NULL, quantiles = c(0.1, 0.5, 0.9), all = TRUE, obs = 1, ...)## S3 method for class 'quantregRanger' predict(object, data = NULL, quantiles = c(0.1, 0.5, 0.9), all = TRUE, obs = 1, ...)
object |
|
data |
New test data of class |
quantiles |
Numeric vector of quantiles that should be estimated |
all |
A logical value. all=TRUE uses all observations for prediction. all=FALSE uses only a certain number of observations per node for prediction (set with argument obs). The default is all=TRUE |
obs |
An integer number. Determines the maximal number of observations per node |
... |
Currently ignored. to use for prediction. The input is ignored for all=TRUE. The default is obs=1 |
A matrix. The first column contains the conditional quantile estimates for the first entry in the vector quantiles. The second column contains the estimates for the second entry of quantiles and so on.
Creates a quantile regression forest like described in Meinshausen, 2006.
quantregRanger(formula = NULL, data = NULL, params.ranger = NULL)quantregRanger(formula = NULL, data = NULL, params.ranger = NULL)
formula |
Object of class |
data |
Training data of class |
params.ranger |
List of further parameters that should be passed to ranger.
See |
Philipp Probst
Meinshausen, Nicolai. "Quantile regression forests." The Journal of Machine Learning Research 7 (2006): 983-999.
y = rnorm(150) x = cbind(y + rnorm(150), rnorm(150)) data = data.frame(x,y) mod = quantregRanger(y ~ ., data = data, params.ranger = list(mtry = 2)) predict(mod, data = data[1:5, ], quantiles = c(0.1, 0.5, 0.9))y = rnorm(150) x = cbind(y + rnorm(150), rnorm(150)) data = data.frame(x,y) mod = quantregRanger(y ~ ., data = data, params.ranger = list(mtry = 2)) predict(mod, data = data[1:5, ], quantiles = c(0.1, 0.5, 0.9))