RVls: Least-square approach to proportion of the explained...

In hychen-uic/TEV: Total Explained Variation

Least-square approach to proportion of the explained variation

Description

This function uses least-square estimates in computing the proportion of the explained variation.

Usage

RVls(y, x, lam = 1, alpha = c(0.05))

Arguments

y	outcome: a vector of length n.
x	covariates: a matrix of nxp dimension.
lam	parameter for altering the weighting matrix.
alpha	a vector of type I errors used to generate (1-alpha)confidence intervals.

Details

This method works only for the case n>p. It uses the least-square approach for the estimation. Covariates are allowed to be correlated.

Value

Estimate of proportion of the explained variation, variance estimates under normality and non-normality error, and the corresponding confidence intervals.

References

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

Chen, H. Y., Li, H., Argos, M., Persky, V. W., and Turyk, M. (2022). Statistical Methods for Assessing Explained Variation of a Health Outcome by Mixture of Exposures. International Journal of Environmental Research and Public Health.

Examples

## Not run: RVls(y,x)

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