rbvs (version 1.0.2)

lasso.coef: Measure an impact of the covariates on the response using Lasso This function evaluates the Lasso coefficients regressing y onto the design matrix x over subsamples in subsamples.

Description

Measure an impact of the covariates on the response using Lasso This function evaluates the Lasso coefficients regressing y onto the design matrix x over subsamples in subsamples.

Usage

lasso.coef(x, y, subsamples, nonzero = NULL, family = c("gaussian", "binomial"), alpha = 1, maxit = 500, tol = 0.01, lambda.ratio = 1e-06, nlam = 25, ...)

Arguments

x

Matrix with n observations of p covariates in each row.

y

Response vector with n observations.

subsamples

Matrix with m indices of N subsamples in each column.

nonzero

Number of non-zero coefficients estimated for each subsample.

family

Determines the likelihood optimised in the estimation procedure.

alpha

Scalar between 0 and 1 determining l2 penalty (see details).

maxit

Maximum number of itarations when computing the lasso coefficients.

tol

Scalar determining convergence of the lasso algorithm used.

lambda.ratio

Scalar being a fraction of 1. Used in the lasso algorithm

nlam

Number of penalty parameters used in the lasso algorithm.

...

Not in use.

Details

To solve the Lasso problem, we implement the coordinate descent algorithm as in Breheny Jian (2011).

References

Tibshirani, Robert. "Regression shrinkage and selection via the lasso." Journal of the Royal Statistical Society. Series B (Methodological) (1996): 267-288.

Breheny, Patrick, and Jian Huang. "Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection." The Annals of Applied Statistics 5.1 (2011): 232.