predict.orf {orf}

Prediction of the Ordered Forest

Description

Prediction for new observations based on estimated Ordered Forest of class orf

Usage

## S3 method for class 'orf'
predict(object, newdata = NULL, type = NULL, inference = NULL, ...)

Arguments

object

estimated Ordered Forest object of class orf

newdata

numeric matrix X containing the observations for which the outcomes should be predicted

type

string, specifying the type of the prediction, These can be either "probs" or "p" for probabilities and "class" or "c" for classes. (Default is "probs").

inference

logical, if TRUE variances for the predictions will be estimated (only feasible for probability predictions).

...

further arguments (currently ignored)

Details

predict.orf estimates the conditional ordered choice probabilities, i.e. P[Y=m|X=x] for new data points (matrix X containing new observations of covariates) based on the estimated Ordered Forest object of class orf. Furthermore, weight-based inference for the probability predictions can be conducted as well. If inference is desired, the supplied Ordered Forest must be estimated with honesty and subsampling. If prediction only is desired, estimation without honesty and with bootstrapping is recommended for optimal prediction performance. Additionally to the probability predictions, class predictions can be estimated as well using the type argument. In this case, the predicted classes are obtained as classes with the highest predicted probability.

Value

object of class orf.prediction with following elements

info

info containing forest inputs and data used

predictions

predicted values

variances

variances of predicted values

Author(s)

Gabriel Okasa

See Also

summary.orf.prediction, print.orf.prediction

Examples

# Ordered Forest
require(orf)

# load example data
data(odata)

# specify response and covariates for train and test
idx <- sample(seq(1, nrow(odata), 1), 0.8*nrow(odata))

# train set
Y_train <- as.numeric(odata[idx, 1])
X_train <- as.matrix(odata[idx, -1])

# test set
Y_test <- as.numeric(odata[-idx, 1])
X_test <- as.matrix(odata[-idx, -1])

# estimate Ordered Forest
orf_fit <- orf(X_train, Y_train)

# predict the probabilities with the estimated orf
orf_pred <- predict(orf_fit, newdata = X_test)

# predict the probabilities with estimated orf together with variances
orf_pred <- predict(orf_fit, newdata = X_test, inference = TRUE)

# predict the classes with estimated orf
orf_pred <- predict(orf_fit, newdata = X_test, type = "class")