mcplus.coef: Measure an impact of the covariates on the response using...

In rbvs: Ranking-Based Variable Selection

Description

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

Usage

mcplus.coef(x, y, subsamples, nonzero = NULL, family = c("gaussian",
  "binomial"), alpha = 1, gamma = 3, 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).
gamma	Scalar greater than 1. The concacivity parameter (see details).
maxit	Maximum number of itarations when computing the MC+ coefficients.
tol	Scalar determining convergence of the MC+ algorithm used.
lambda.ratio	Scalar being a fraction of 1. Used in the MC+ algorithm
nlam	Number of penalty parameters used in the MC+ algorithm.
...	Not in use.

Details

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

Author(s)

Rafal Baranowski, Patrick Breheny

References

Zhang, Cun-Hui. "Nearly unbiased variable selection under minimax concave penalty." The Annals of Statistics (2010): 894-942.

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.

rbvs documentation built on May 2, 2019, 7:31 a.m.