Returns probabilities from the estimated conditional cumulative distribution function of the prediction error associated with each test observation.
A vector of quantiles.
A vector of the indices of the test observations for which the
conditional error CDFs are desired. Defaults to all test observations
given in the call of
This function is only defined as output of the
It is not exported as a standalone function. See the example.
xs has length one, then a vector is
returned with the desired probabilities. If both have length greater than
one, then a
data.frame of probabilities is returned, with rows
corresponding to the inputted
xs and columns corresponding to the
<email@example.com>; Johanna Hardin
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# 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 probability that the error associated with each test # prediction is less than -4 and the probability that the error # associated with each test prediction is less than 7 output$perror(c(-4, 7)) # same as above but only for the first three test observations output$perror(c(-4, 7), 1:3)
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