predict.ordfor: Prediction using ordinal forest objects

In ordinalForest: Ordinal Forests: Prediction and Variable Ranking with Ordinal Target Variables

Prediction using ordinal forest objects

Description

Prediction of test data using ordinal forest.

Usage

## S3 method for class 'ordfor'
predict(object, newdata, ...)

Arguments

object	object of class ordfor. See function ordfor.
newdata	data.frame. Data frame containing new data.
...	further arguments passed to or from other methods.

Value

predict.ordfor returns an object of class ordforpred. An object of class "ordforpred" is a list containing the following components:

ypred	vector of length nrow(newdata). Factor-valued test data predictions.
classprobs	predicted class probabilities. Only provided, if the performance function based on the ranked probability score was used, while training the ordinal forest (see ordfor). Matrix of dimension nrow(newdata) x J (NA, if the ranked probability was not used while training). The value in the j-th column of the i-th row contains the predicted probability that test observation i is of class j.

References

• Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. <doi: 10.1007/s00357-018-9302-x>.

Examples

## Not run: 
data(hearth)

set.seed(123)
trainind <- sort(sample(1:nrow(hearth), size=floor(nrow(hearth)*(1/2))))
testind <- sort(sample(setdiff(1:nrow(hearth), trainind), size=20))

datatrain <- hearth[trainind,]
datatest <- hearth[testind,]

ordforres <- ordfor(depvar="Class", data=datatrain, perffunction = "probability", nsets=50,
  nbest=5, ntreeperdiv=100, ntreefinal=1000)
# NOTE: nsets=50 is not enough, because the prediction performance of the resulting 
# ordinal forest will be suboptimal!! In practice, nsets=1000 (default value) or a larger
# number should be used.

preds <- predict(ordforres, newdata=datatest)
preds
 
table(data.frame(true_values=datatest$Class, predictions=preds$ypred))

head(preds$classprobs)

## End(Not run)

ordinalForest documentation built on Dec. 1, 2022, 1:25 a.m.