predict.super_RaSE: Predict the outcome of new observations based on the...

In RaSEn: Random Subspace Ensemble Classification and Variable Screening

Description

Predict the outcome of new observations based on the estimated super RaSE classifier (Zhu, J. and Feng, Y., 2021).

Usage

## S3 method for class 'super_RaSE'
predict(object, newx, type = c("vote", "prob", "raw-vote", "raw-prob"), ...)

Arguments

object	fitted 'super_RaSE' object using Rase.
newx	a set of new observations. Each row of newx is a new observation.
type	the type of prediction output. Can be 'vote', 'prob', 'raw-vote' or 'raw-prob'. Default = 'vote'. vote: output the predicted class (by voting and cut-off) of new observations. Avalilable for all base learner types. prob: output the predicted probabilities (posterior probability of each observation to be class 1) of new observations. It is the average probability over all base learners. raw-vote: output the predicted class of new observations for all base learners. It is a n by B1 matrix. n is the test sample size and B1 is the number of base learners used in RaSE. Avalilable for all base learner types. raw-prob: output the predicted probabilities (posterior probability of each observation to be class 1) of new observations for all base learners. It is a n by B1 matrix.
...	additional arguments.

Value

depends on the parameter type. See the list above.

References

Zhu, J. and Feng, Y., 2021. Super RaSE: Super Random Subspace Ensemble Classification. https://www.preprints.org/manuscript/202110.0042

See Also

Rase.

Examples

## Not run: 
set.seed(0, kind = "L'Ecuyer-CMRG")
train.data <- RaModel("classification", 1, n = 100, p = 50)
test.data <- RaModel("classification", 1, n = 100, p = 50)
xtrain <- train.data$x
ytrain <- train.data$y
xtest <- test.data$x
ytest <- test.data$y

# fit a super RaSE classifier by sampling base learner from kNN, LDA and
# logistic regression in equal probability
fit <- Rase(xtrain = xtrain, ytrain = ytrain, B1 = 100, B2 = 100,
base = c("knn", "lda", "logistic"), super = list(type = "separate", base.update = T),
criterion = "cv", cv = 5, iteration = 1, cores = 2)
ypred <- predict(fit, xtest)
mean(ypred != ytest)

## End(Not run)

RaSEn documentation built on Oct. 16, 2021, 9:06 a.m.