hct_method_corr {HDDesign} | R Documentation |
Determine the probability of correct classification (PCC) for studies employing high dimensional features for classification. Higher Criticisms Threshold (HCT) classifier is used to choose the p-value threshold for feature selection. In addition to the original HCT procedure by (Donoho and Jin 2009), two more procedures to choose p-value threshold have developed and implemented.
hct_method_corr(mu0, p, m, n, hct, alpha0, nrep, p1 = 0.5,
ss = F, pcorr, chol.rho, 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. |
hct |
The HCT procedure employed to choose the p-value threshold for feature selection. There are two valid choices (case sensitive): 1) hct_empirical, the HCT procedure originally proposed by (Donoho and Jin 2009); 2) hct_beta, an alternative HCT procedure which makes use of the beta distribution of the p-values under the null |
alpha0 |
The proportion of the smallest p-values we will consider in the HCT algorithm. |
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. |
pcorr |
Number of correlated features. |
chol.rho |
Cholesky decomposition of the covariance of the pcorr features that are correlated. It is assumed that the m important features are part of the pcorr correlated features. |
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. |
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>
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) (November 13): 4449-4470.
## Sigma_1 in the paper
#first block is pcorr x pcorr of compound symmetry
#other diagonal block is Identity; off diagonal blocks are 0
pcorr=10
p=500
rho.cs=.8
#create first block
rho= diag(c((1-rho.cs)*rep(1,pcorr),rep(1,p-pcorr)))+ matrix(c(rho.cs*
rep(1,pcorr),rep(0,p-pcorr)), ncol=1) %*% c(rep(1,pcorr),rep(0,p-pcorr))
chol.rho1.500=chol(rho[1:pcorr,1:pcorr])
set.seed(1)
hct_method_corr(mu0=0.4,p=500,m=10,n=80,hct=hct_beta,alpha0=0.5,nrep=10,
p1=0.5,ss=TRUE,pcorr=pcorr,chol.rho=chol.rho1.500)
#return: 0.6672256 0.6672256 0.6672256