hct_empirical {HDDesign} | R Documentation |
Original HCT Procedure to Choose P-Value Threshold for Feature Selection
Description
This is the original Higher Criticism Threshold (HCT) procedure (Donoho and Jin 2009) to choose p-value threshold for feature selection. Only the features whose p-values are less than the thresold will be included in the classifier.
Usage
hct_empirical(pvalue, p, n)
Arguments
pvalue |
A vector containing the p*alpha_0 smallest p-values. |
p |
The number of the features in total. |
n |
The total sample size for the two groups. |
Details
Refer to (Donoho and Jin 2009)
Value
The p-value threshold for feature selection. Only the features whose p-values are less than the thresold 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
Donoho, D. and Jin, J. 2009. "Feature Selection by Higher Criticism Thresholding Achieves the Optimal Phase Diagram." Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences 367 (1906): 4449-4470.
Examples
hct_empirical(pvalue=0.10,p=500,n=80)
# 0.1