scadglm {SIS} | R Documentation |
These functions solve SCAD regularized loglikelihood for generalized linear models; scadcox
does the
one-step SCAD while fullscadcox
solves the SCAD in a fully
iterative method.
scadglm(x, y, wt.initsoln=NULL, lambda, initsoln=NULL, family = binomial(), weight = NULL, offset = NULL, function.precision=1e-8, nopenalty.subset=NULL) fullscadglm(x, y, lambda, initsoln=NULL, family = binomial(), weight = NULL, offset = NULL, function.precision=1e-8, nopenalty.subset=NULL, eps0=1e-6)
x |
an (n * p) matrix of features. |
y |
an (n) vector of response. |
wt.initsoln |
a (p+1) vector of initial solution for one-step SCAD. |
lambda |
regularization parameter for the SCAD. |
initsoln |
a (p+1) vector of initial solution. |
family |
a description of the error distribution and link function to be used in the model. |
weight |
an optional (n) vector of weights to be used in the fitting process. |
offset |
this can be used to specify an a priori known component to be included in the linear predictor during fitting.
|
function.precision |
function.precision parameter used in the internal
solver. Default is 1e-8.
|
nopenalty.subset |
a set of indices for the predictors that are not subject to the L1 penalty. |
eps0 |
an effective zero. |
They return a (p+1) vector of estimated coefficients.
Jianqing Fan, Yang Feng, Richard Samworth, and Yichao Wu
Jianqing Fan and Runze Li (2001) Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of American Statistical Association, 96, 1348-1360.
Hui Zou and Runze Li (2008) One-step Sparse Estimates in Nonconcave Penalized Likelihood Models (with discussion). The Annals of Statistics, 36, 1509-1533
set.seed(0) b <- c(1,1,1,-3*sqrt(2)/2) n=400 p=30 truerho=0.5 x=matrix(rnorm(n*p, mean=0, sd=1), n, p) feta=x[, 1:4]%*%b fprob=exp(feta)/(1+exp(feta)) y=rbinom(n, 1, fprob) scadglm(x,y,lambda=0.0015) coef(glm(y~x,family=binomial()))