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Description

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

Usage

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RPalpha(RP.out, Y, p1)

Arguments

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

Details

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

Value

alpha

The value of alpha that minimises the empirical error

Author(s)

Timothy I. Cannings and Richard J. Samworth

References

Cannings, T. I. and Samworth, R. J. (2015) Random projection ensemble classification. http://arxiv.org/abs/1504.04595

See Also

RPParallel

Examples

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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