EigenPrismFull: Estimating proportion of explained variation using the...

In hychen-uic/TEV: Total Explained Variation

Estimating proportion of explained variation using the least-square approach or the EigenPrism approach

Description

EigenPrismFull procedure integrating the n\le p and n>p cases

Usage

EigenPrismFull(y, x, alpha = c(0.01, 0.05, 0.1))

Arguments

y	outcome: a vector of length n.
x	covariates: a matrix of nxp dimension.
alpha	significance level: a vector.

Details

For n\le p, the estimate and confidence interval are obtained by EigenPrism approach. For n>p, the estimate is obtained by least-square approach, and the confidence intervals are obtained by inverting the chisquare test.

Value

Estimate of the proportion of the explained variation and confidence intervals for the proportion.

References

Chen, H.Y. (2022). Statistical inference on explained variation in high-dimensional linear model with dense effects. arXiv:2201.08723

Janson, L., Barber, R. F., Candes, E. (2017). EigenPrism: inference for high-dimensional signal-to-noise ratios. Journal of Royal Statistical Society, Ser. B., 79, 1037-1065.

Lucas Janson. http://lucasjanson.fas.harvard.edu/code/EigenPrism.R.

Examples

## Not run: EigenPrismFull(y,x)

hychen-uic/TEV documentation built on Jan. 24, 2025, 9:14 p.m.