| hct_beta {HDDesign} | R Documentation |
Alternative HCT Procedure to Choose P-Value Threshold Based on Beta Distribution of P-Values.
Description
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.
Usage
hct_beta(pvalue, p, n)
Arguments
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. |
Details
Refer to Sanchez, et al (2016), Section 3 and supplementary materials.
Value
The p-value threshold for feature selection. Only the features whose p-values are less than the threshold will be included in the classifier.
Author(s)
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>
References
Sanchez, B.N., Wu, M., Song, P.X.K., and Wang W. (2016). "Study design in high-dimensional classification analysis." Biostatistics, in press.
Examples
hct_beta(pvalue=0.10,p=500,n=80)
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