hct_empirical: Original HCT Procedure to Choose P-Value Threshold for...

In HDDesign: Sample Size Calculation for High Dimensional Classification Study

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 <[e-mail]> Brisa N. Sanchez <[e-mail]> Peter X.K. Song <[e-mail]> Raymond Luu <[e-mail]> Wen Wang <[e-mail]>

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

Example output

[1] 0.1

HDDesign documentation built on May 2, 2019, 6:41 a.m.