| Title: | A Robust Integrated Mean Variance Correlation |
|---|---|
| Description: | Measure the dependence structure between two random variables with IMVC and extend IMVC to hypothesis test, feature screening and FDR control. |
| Authors: | Wei Xiong [aut], Han Pan [aut, cre], Hengjian Cui [aut] |
| Maintainer: | Han Pan <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.0 |
| Built: | 2025-02-12 04:10:32 UTC |
| Source: | https://github.com/scottpanhan/newimvc |
This function is used to calculate the integrated mean variance correlation between two vectors
IMVC(y, x, K, NN = 3, type)IMVC(y, x, K, NN = 3, type)
y |
is a numeric vector |
x |
is a numeric vector |
K |
is the number of quantile levels |
NN |
is the number of B spline basis, default is 3 |
type |
is an indicator for measuring linear or nonlinear correlation, "linear" represents linear correlation and "nonlinear" represents linear or nonlinear correlation using B splines |
This function is used for FDR control with integrated mean variance correlation
IMVCFDR(y, x, K, NN = 3, numboot, timeboot, true_signal, null_method, alpha)IMVCFDR(y, x, K, NN = 3, numboot, timeboot, true_signal, null_method, alpha)
y |
is the response vector |
x |
is the covariate matrix |
K |
is the number of quantile levels |
NN |
is the number of B spline basis, default is 3 |
numboot |
is the size of bootstrap samples |
timeboot |
is the number of bootstrap times for computing standard deviation of the IMVC |
true_signal |
is the true active set |
null_method |
is the estimation method for proportion of true null hypotheses. Choices are "lfdr", "mean", "hist" or "convest" |
alpha |
is the nominal FDR level |
A list of FDP, power and selected variables
This function is used to select important features using integrated mean variance correlation
IMVCS(y, x, K, d, NN = 3, type)IMVCS(y, x, K, d, NN = 3, type)
y |
is the response vector |
x |
is the covariate matrix |
K |
is the number of quantile levels |
d |
is the size of selected variables |
NN |
is the number of B spline basis, default is 3 |
type |
is an indicator for measuring linear or nonlinear correlation, "linear" represents linear correlation and "nonlinear" represents linear or nonlinear correlation using B splines |
This function is used to test significance of linear or nonlinear correlation using integrated mean variance correlation
IMVCT(x, y, K, num_per, NN = 3, type)IMVCT(x, y, K, num_per, NN = 3, type)
x |
is the univariate covariate vector |
y |
is the response vector |
K |
is the number of quantile levels |
num_per |
is the number of permutation times |
NN |
is the number of B spline basis, default is 3 |
type |
is an indicator for measuring linear or nonlinear correlation, "linear" represents linear correlation and "nonlinear" represents linear or nonlinear correlation using B splines |