predict.quantregForest: Prediction method for class quantregForest
In quantregForest: Quantile Regression Forests

Description Usage Arguments Value Author(s) See Also Examples

Prediction of test data with quantile regression forests.

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## S3 method for class 'quantregForest'
predict(object, newdata = NULL, 
                         what = c(0.1, 0.5, 0.9), ...)

`object`	An object of class `quantregForest`
`newdata`	A data frame or matrix containing new data. If left at default `NULL`, the out-of-bag predictions (OOB) are returned, for which the option `keep.inbag` has to be set to `TRUE` at the time of fitting the object.
`what`	Can be a vector of quantiles or a function. Default for `what` is a a vector of quantiles (with numerical values in [0,1]) for which the conditional quantile estimates should be returned. If a function it has to take as argument a numeric vector and return either a summary statistic (such as `mean`,`median` or `sd` to get conditional mean, median or standard deviation) or a vector of values (such as with `quantiles` or via `sample`) or a function (for example with `ecdf`).
`...`	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