RPalpha: Choose alpha
In RPEnsemble: Random Projection Ensemble Classification

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/RPalpha.R

Chooses the best empirical value of the cutoff alpha, based on the leave-one-out, resubstitution or sample-split estimates of the class labels.

1	RPalpha(RP.out, Y, p1)

`RP.out`	The result of a call to `RPParallel`
`Y`	Vector of length `n` or `n.val` containing the training or validation dataset classes
`p1`	(Empirical) prior probability

See precise details in Cannings and Samworth (2015, Section 5.1).

alpha

The value of alpha that minimises the empirical error

Timothy I. Cannings and Richard J. Samworth

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

RPParallel

Train <- RPModel(1, 50, 100, 0.5)
Test <- RPModel(1, 100, 100, 0.5)
Out <- RPParallel(XTrain = Train$x, YTrain = Train$y, XTest = Test$x, d = 2, B1 = 10, 
B2 = 10, base = "LDA", projmethod = "Haar", estmethod = "training", cores = 1)
alpha <- RPalpha(RP.out = Out, Y = Train$y, p1 = sum(Train$y == 1)/length(Train$y))
alpha