Description Usage Arguments Details Value Author(s) See Also Examples

Returns quantiles of the estimated conditional error distribution associated with each test prediction.

1 | ```
qerror(p, xs)
``` |

`p` |
A vector of probabilities. |

`xs` |
A vector of the indices of the test observations for which the
conditional error quantiles are desired. Defaults to all test observations
given in the call of |

This function is only defined as output of the `quantForestError`

function.
It is not exported as a standalone function. See the example.

If either `p`

or `xs`

has length one, then a vector is
returned with the desired quantiles. If both have length greater than
one, then a `data.frame`

of quantiles is returned, with rows
corresponding to the inputted `xs`

and columns corresponding to the
inputted `p`

.

Benjamin Lu `<b.lu@berkeley.edu>`

; Johanna Hardin `<jo.hardin@pomona.edu>`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
# 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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