# NOT RUN {
# load data
data(airquality)
# remove observations with missing predictor variable values
airquality <- airquality[complete.cases(airquality), ]
# get number of observations and the response column index
n <- nrow(airquality)
response.col <- 1
# split data into training and test sets
train.ind <- sample(1:n, n * 0.9, replace = FALSE)
Xtrain <- airquality[train.ind, -response.col]
Ytrain <- airquality[train.ind, response.col]
Xtest <- airquality[-train.ind, -response.col]
Ytest <- airquality[-train.ind, response.col]
# fit random forest to the training data
rf <- randomForest::randomForest(Xtrain, Ytrain, nodesize = 5,
ntree = 500,
keep.inbag = TRUE)
# estimate conditional error distribution functions
output <- quantForestError(rf, Xtrain, Xtest,
what = c("p.error", "q.error"))
# get the 0.25 and 0.8 quantiles of the error distribution
# associated with each test prediction
output$qerror(c(0.25, 0.8))
# same as above but only for the first three test observations
output$qerror(c(0.25, 0.8), 1:3)
# }
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