RPChoose {RPEnsemble}

Chooses projection

Description

Chooses a the best projection from a set of size B2 based on a test error estimate, then classifies the training and test sets using the chosen projection.

Usage

RPChoose(XTrain, YTrain, XTest, d, B2 = 10, base = "LDA", k = c(3,5), 
projmethod = "Haar", estmethod = "training", ...)

Arguments

XTrain

An n by p matrix containing the training data feature vectors

YTrain

A vector of length n of the classes (either 1 or 2) of the training data

XTest

An n.test by p matrix of the test data

d

The lower dimension of the image space of the projections

B2

The block size

base

The base classifier one of "knn","LDA","QDA" or "other"

k

The options for k if base is "knn"

projmethod

Either "Haar", "Gaussian" or "axis"

estmethod

Method for estimating the test errors to choose the projection: either training error "training" or leave-one-out "loo"

...

Optional further arguments if base = "other"

Details

Randomly projects the the data B2 times. Chooses the projection yielding the smallest estimate of the test error. Classifies the training set (via the same method as estmethod) and test set using the chosen projection.

Value

Returns a vector of length n + n.test: the first n entries are the estimated classes of the training set, the last n.test are the estimated classes of the test set.

Note

Resubstitution method unsuitable for the k-nearest neighbour classifier.

Author(s)

Timothy I. Cannings and Richard J. Samworth

References

Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959–1035

See Also

RPParallel, RPChooseSS, lda, qda, knn

Examples

set.seed(100)
Train <- RPModel(1, 50, 100, 0.5)
Test <- RPModel(1, 100, 100, 0.5)
Choose.out5 <- RPChoose(XTrain = Train$x, YTrain = Train$y, XTest = Test$x, 
d = 2, B2 = 5, base = "QDA", projmethod = "Haar", estmethod = "loo")
Choose.out10 <- RPChoose(XTrain = Train$x, YTrain = Train$y, XTest = Test$x, 
d = 2, B2 = 10, base = "QDA", projmethod = "Haar", estmethod = "loo")
sum(Choose.out5[1:50] != Train$y)
sum(Choose.out10[1:50] != Train$y)
sum(Choose.out5[51:150] != Test$y)
sum(Choose.out10[51:150] != Test$y)