quantregForest {quantregForest} | R Documentation |
Quantile Regression Forests infer conditional quantile functions from data
quantregForest(x, y, mtry = ceiling(ncol(x)/3), nodesize = 10, ntree = 1000)
x |
A matrix or data.frame containing the predictor variables |
y |
The response variable; a numerical vector |
mtry |
The number of variables to try for each split; same default setting as for Random Forests |
nodesize |
The minimal number of instances in each terminal node; the default setting is slightly higher than for Random Forests |
ntree |
The number of trees to be grown |
It might be useful to try various values of mtry
and see which
one works best;
however, results are typically not heavily dependent
on this parameter.
A value of class quantregForest
, for which print
, plot
, and
predict
methods are available.
Nicolai Meinshausen
N. Meinshausen (2006) "Quantile Regression Forests", Journal of Machine Learning Research 7, 983-999 http://jmlr.csail.mit.edu/papers/v7/
For prediction, see predict.quantregForest
################################################ ## Load air-quality data (and preprocessing) ## ################################################ data(airquality) set.seed(1) ## remove observations with mising values airquality <- airquality[ !apply(is.na(airquality), 1,any), ] ## number of remining samples n <- nrow(airquality) ## divide into training and test data indextrain <- sample(1:n,round(0.6*n),replace=FALSE) Xtrain <- airquality[ indextrain,2:6] Xtest <- airquality[-indextrain,2:6] Ytrain <- airquality[ indextrain,1] Ytest <- airquality[-indextrain,1] ################################################ ## compute Quantile Regression Forests ## ################################################ qrf <- quantregForest(x=Xtrain, y=Ytrain) ## plot out-of-bag predictions for the training data plot(qrf) ## compute out-of-bag predictions quant.outofbag <- predict(qrf) ## predict test data quant.newdata <- predict(qrf, newdata= Xtest)