Description Usage Arguments Details Value Author(s) References Examples
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.
1 | cv_method(mu0, p, m, n, alpha_list, nrep, p1 = 0.5, ss = F, sampling.p=0.5)
|
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. |
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.
If ss=FALSE, the function returns the expected PCC. If ss=TRUE, the function returns a vector containing the expected PCC, sensitivity and specificity.
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.
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