This procedure chooses the p-value threshold for feature selection in a similar fashion to hct_empirical. However, it is based on the Beta distribution of the p-values. Only the features whose p-values are less than the thresold will be included in the classifier.

1 | ```
hct_beta(pvalue, p, n)
``` |

`pvalue` |
A vector containing the p*alpha_0 smallest p-values; typically alpha_0=0.10 |

`p` |
The number of the features in total. |

`n` |
The total sample size for the two groups. |

Refer to Sanchez, et al (2016), Section 3 and supplementary materials.

The p-value threshold for feature selection. Only the features whose p-values are less than the threshold will be included in the classifier.

Meihua Wu <meihuawu@umich.edu> Brisa N. Sanchez <brisa@umich.edu> Peter X.K. Song <pxsong@umich.edu> Raymond Luu <raluu@umich.edu> Wen Wang <wangwen@umich.edu>

Sanchez, B.N., Wu, M., Song, P.X.K., and Wang W. (2016). "Study design in high-dimensional classification analysis." Biostatistics, in press.

1 2 | ```
hct_beta(pvalue=0.10,p=500,n=80)
# 0.1
``` |

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