| cv_method {HDDesign} | R Documentation |
Formula-based PCC of a CV-based classifier
Description
Determine the probability of correct classification (PCC) for a high dimensional classification study employing cross validation to determine an optimal p-value cutoff to select features that are included in a linear classifier.
Usage
cv_method(mu0, p, m, n, alpha_list, nrep, p1 = 0.5, ss = F, sampling.p=0.5)
Arguments
mu0 |
The effect size of the important features. |
p |
The number of the features in total. |
m |
The number of the important features. |
n |
The total sample size for the two groups. |
alpha_list |
The search grid for the p-value threshold. |
nrep |
The number of simulation replicates employed to compute the expected PCC and/or sensitivity and specificity. |
p1 |
The prevalence of the group 1 in the population, default to 0.5. |
ss |
Boolean variable, default to FALSE. The TRUE value instruct the program to compute the sensitivity and the specificity of the classifier. |
sampling.p |
The assumed proportion of group 1 samples in the training data; default of 0.5 assumes groups are equally represented regardless of p1. |
Details
Refer to Sanchez, Wu, Song, Wang 2016, Section 2.2 for the details of the algorithm. This function was used to produce Figure 2 of the paper.
Value
If ss=FALSE, the function returns the expected PCC. If ss=TRUE, the function returns a vector containing the expected PCC, sensitivity and specificity.
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
set.seed(1)
cv_method(mu0=0.4, p=500, m=10, n=80, alpha_list=c(0.0000001, 0.0001, 0.01),
nrep=10, p1=0.6, ss=TRUE)
#return: 0.8012142 0.8210082 0.7714031
#alpha_list should be a dense list of p-value cutoffs;
#here we only use a few values to ease computation of the example.