wtlassoglm {SIS} | R Documentation |
This functions solves weighted L1 regularized loglikelihood for generalized linear models.
wtlassoglm(x, y, lassoweight=NULL, initsoln=NULL, family = binomial(), weight = NULL, offset = NULL, lambda2=0, function.precision=1e-8)
x |
an (n * p) matrix of features. |
y |
an (n) vector of response. |
lassoweight |
a (p) vector of weights specifying the weighted L1 penalty. |
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.
|
lambda2 |
regularization parameter for the L2 norm of the
coefficients. Default is 0.
|
function.precision |
function.precision parameter used in the internal
solver. Default is 1e-8.
|
This function solves weighted L1 regularized loglikelihood for generalized linear models.
It is based on the source code of R package glmpath
.
An object is returned with
lambda2 |
λ_2 used. |
xnames |
column names of x. |
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.
|
lassoweight |
a (p) vector of weights specifying the weighted L1 penalty. |
initsoln |
a (p+1) vector of initial solution. |
w |
a (p+1) vector of weight L1 solution. |
Jianqing Fan, Yang Feng, Richard Samworth, and Yichao Wu
set.seed(0) b <- c(2,2,2,-3*sqrt(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) lassoweight<-rep(0.6,30) wtlassoglm(x,y,lassoweight)$w coef(glm(y~x,family=binomial()))