hct_beta: Alternative HCT Procedure to Choose P-Value Threshold Based...
In HDDesign: Sample Size Calculation for High Dimensional Classification Study

Description Usage Arguments Details Value Author(s) References Examples

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.