| Title: | Quantile Regression Forests |
|---|---|
| Description: | Quantile Regression Forests is a tree-based ensemble method for estimation of conditional quantiles. It is particularly well suited for high-dimensional data. Predictor variables of mixed classes can be handled. The package is dependent on the package 'randomForest', written by Andy Liaw. |
| Authors: | Nicolai Meinshausen [aut], Loris Michel [cre] |
| Maintainer: | Loris Michel <[email protected]> |
| License: | GPL |
| Version: | 1.3-7.1 |
| Built: | 2024-11-07 06:14:55 UTC |
| Source: | CRAN |
Prediction of test data with quantile regression forests.
## S3 method for class 'quantregForest' predict(object, newdata = NULL, what = c(0.1, 0.5, 0.9), ...)## S3 method for class 'quantregForest' predict(object, newdata = NULL, what = c(0.1, 0.5, 0.9), ...)
object |
An object of class |
newdata |
A data frame or matrix containing new data. If left at default |
what |
Can be a vector of quantiles or a function. Default for If a function it has to take as argument a numeric vector and return either a summary statistic (such as |
... |
Additional arguments (currently not in use). |
A vector, matrix or list.
If what is a vector with desired quantiles (the default is what=c(0.1,0.5,0.9)), a matrix with one column per requested quantile returned.
If just a single quantile is specified (for example via what=0.5), a vector is returned.
If what is a function with numerical return value (for example via what=function(x) sample(x,10,replace=TRUE) to sample 10 new observations from conditional distribution), the output is also a matrix (or vector if just a scalar is returned).
If what has a function as output (such as what=ecdf), a list will be returned with one element per new sample point (and the element contains the desired function).
Nicolai Meinshausen, Christina Heinze
quantregForest,
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) qrf <- quantregForest(x=Xtrain, y=Ytrain, nodesize=10,sampsize=30) ## predict 0.1, 0.5 and 0.9 quantiles for test data conditionalQuantiles <- predict(qrf, Xtest) print(conditionalQuantiles[1:4,]) ## predict 0.1, 0.2,..., 0.9 quantiles for test data conditionalQuantiles <- predict(qrf, Xtest, what=0.1*(1:9)) print(conditionalQuantiles[1:4,]) ## estimate conditional standard deviation conditionalSd <- predict(qrf, Xtest, what=sd) print(conditionalSd[1:4]) ## estimate conditional mean (as in original RF) conditionalMean <- predict(qrf, Xtest, what=mean) print(conditionalMean[1:4]) ## sample 10 new observations from conditional distribution at each new sample newSamples <- predict(qrf, Xtest,what = function(x) sample(x,10,replace=TRUE)) print(newSamples[1:4,]) ## get ecdf-function for each new test data point ## (output will be a list with one element per sample) condEcdf <- predict(qrf, Xtest, what=ecdf) condEcdf[[10]](30) ## get the conditional distribution at value 30 for i=10 ## or, directly, for all samples at value 30 (returns a vector) condEcdf30 <- predict(qrf, Xtest, what=function(x) ecdf(x)(30)) print(condEcdf30[1:4]) ## to use other functions of the package randomForest, convert class back class(qrf) <- "randomForest" importance(qrf) ## importance measure from the standard RF################################################ ## 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) qrf <- quantregForest(x=Xtrain, y=Ytrain, nodesize=10,sampsize=30) ## predict 0.1, 0.5 and 0.9 quantiles for test data conditionalQuantiles <- predict(qrf, Xtest) print(conditionalQuantiles[1:4,]) ## predict 0.1, 0.2,..., 0.9 quantiles for test data conditionalQuantiles <- predict(qrf, Xtest, what=0.1*(1:9)) print(conditionalQuantiles[1:4,]) ## estimate conditional standard deviation conditionalSd <- predict(qrf, Xtest, what=sd) print(conditionalSd[1:4]) ## estimate conditional mean (as in original RF) conditionalMean <- predict(qrf, Xtest, what=mean) print(conditionalMean[1:4]) ## sample 10 new observations from conditional distribution at each new sample newSamples <- predict(qrf, Xtest,what = function(x) sample(x,10,replace=TRUE)) print(newSamples[1:4,]) ## get ecdf-function for each new test data point ## (output will be a list with one element per sample) condEcdf <- predict(qrf, Xtest, what=ecdf) condEcdf[[10]](30) ## get the conditional distribution at value 30 for i=10 ## or, directly, for all samples at value 30 (returns a vector) condEcdf30 <- predict(qrf, Xtest, what=function(x) ecdf(x)(30)) print(condEcdf30[1:4]) ## to use other functions of the package randomForest, convert class back class(qrf) <- "randomForest" importance(qrf) ## importance measure from the standard RF
Quantile Regression Forests infer conditional quantile functions from data
quantregForest(x,y, nthreads=1, keep.inbag=FALSE, ...)quantregForest(x,y, nthreads=1, keep.inbag=FALSE, ...)
x |
A matrix or data.frame containing the predictor variables. |
y |
The response variable. |
nthreads |
The number of threads to use (for parallel computation). |
keep.inbag |
Keep information which observations are in and out-of-bag? For out-of-bag predictions, this argument needs to be set to |
... |
Other arguments passed to |
The object can be converted back into a standard randomForest object and all the functions of the randomForest package can then be used (see example below).
The response y should in general be numeric. However, some use cases exists if y is a factor (such as sampling from conditional distribution when using for example what=function(x) sample(x,10)). Trying to generate quantiles will generate an error if y is a factor, though.
Parallel computation is invoked by setting the value of nthreads to values larger than 1 (for example to the number of available CPUs).
The argument only has an effect under Linux and Mac OSX and is without effect on Windows due to restrictions on forking.
A value of class quantregForest, for which print and predict methods are available.
Class quantregForest is a list of the following components additional to the ones given by class randomForest:
call |
the original call to |
valuesNodes |
a matrix that contains per tree and node one subsampled observation |
Nicolai Meinshausen, Christina Heinze
N. Meinshausen (2006) "Quantile Regression Forests", Journal of Machine Learning Research 7, 983-999 https://jmlr.csail.mit.edu/papers/v7/
################################################ ## 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) qrf <- quantregForest(x=Xtrain, y=Ytrain, nodesize=10,sampsize=30) ## for parallel computation use the nthread option ## qrf <- quantregForest(x=Xtrain, y=Ytrain, nthread=8) ## predict 0.1, 0.5 and 0.9 quantiles for test data conditionalQuantiles <- predict(qrf, Xtest) print(conditionalQuantiles[1:4,]) ## predict 0.1, 0.2,..., 0.9 quantiles for test data conditionalQuantiles <- predict(qrf, Xtest, what=0.1*(1:9)) print(conditionalQuantiles[1:4,]) ## estimate conditional standard deviation conditionalSd <- predict(qrf, Xtest, what=sd) print(conditionalSd[1:4]) ## estimate conditional mean (as in original RF) conditionalMean <- predict(qrf, Xtest, what=mean) print(conditionalMean[1:4]) ## sample 10 new observations from conditional distribution at each new sample newSamples <- predict(qrf, Xtest,what = function(x) sample(x,10,replace=TRUE)) print(newSamples[1:4,]) ## get ecdf-function for each new test data point ## (output will be a list with one element per sample) condEcdf <- predict(qrf, Xtest, what=ecdf) condEcdf[[10]](30) ## get the conditional distribution at value 30 for i=10 ## or, directly, for all samples at value 30 (returns a vector) condEcdf30 <- predict(qrf, Xtest, what=function(x) ecdf(x)(30)) print(condEcdf30[1:4]) ## to use other functions of the package randomForest, convert class back class(qrf) <- "randomForest" importance(qrf) ## importance measure from the standard RF ##################################### ## out-of-bag predictions and sampling ################################## ## for with option keep.inbag=TRUE qrf <- quantregForest(x=Xtrain, y=Ytrain, keep.inbag=TRUE) ## or use parallel version ## qrf <- quantregForest(x=Xtrain, y=Ytrain, nthread=8) ## get quantiles oobQuantiles <- predict( qrf, what= c(0.2,0.5,0.8)) ## sample from oob-distribution oobSample <- predict( qrf, what= function(x) sample(x,1))################################################ ## 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) qrf <- quantregForest(x=Xtrain, y=Ytrain, nodesize=10,sampsize=30) ## for parallel computation use the nthread option ## qrf <- quantregForest(x=Xtrain, y=Ytrain, nthread=8) ## predict 0.1, 0.5 and 0.9 quantiles for test data conditionalQuantiles <- predict(qrf, Xtest) print(conditionalQuantiles[1:4,]) ## predict 0.1, 0.2,..., 0.9 quantiles for test data conditionalQuantiles <- predict(qrf, Xtest, what=0.1*(1:9)) print(conditionalQuantiles[1:4,]) ## estimate conditional standard deviation conditionalSd <- predict(qrf, Xtest, what=sd) print(conditionalSd[1:4]) ## estimate conditional mean (as in original RF) conditionalMean <- predict(qrf, Xtest, what=mean) print(conditionalMean[1:4]) ## sample 10 new observations from conditional distribution at each new sample newSamples <- predict(qrf, Xtest,what = function(x) sample(x,10,replace=TRUE)) print(newSamples[1:4,]) ## get ecdf-function for each new test data point ## (output will be a list with one element per sample) condEcdf <- predict(qrf, Xtest, what=ecdf) condEcdf[[10]](30) ## get the conditional distribution at value 30 for i=10 ## or, directly, for all samples at value 30 (returns a vector) condEcdf30 <- predict(qrf, Xtest, what=function(x) ecdf(x)(30)) print(condEcdf30[1:4]) ## to use other functions of the package randomForest, convert class back class(qrf) <- "randomForest" importance(qrf) ## importance measure from the standard RF ##################################### ## out-of-bag predictions and sampling ################################## ## for with option keep.inbag=TRUE qrf <- quantregForest(x=Xtrain, y=Ytrain, keep.inbag=TRUE) ## or use parallel version ## qrf <- quantregForest(x=Xtrain, y=Ytrain, nthread=8) ## get quantiles oobQuantiles <- predict( qrf, what= c(0.2,0.5,0.8)) ## sample from oob-distribution oobSample <- predict( qrf, what= function(x) sample(x,1))