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 |

`Y` |
Vector of length |

`p1` |
(Empirical) prior probability |

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

`alpha` |
The value of |

Timothy I. Cannings and Richard J. Samworth

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

`RPParallel`

1 2 3 4 5 6 | ```
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
``` |

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