adapt_bic {adapt4pv}  R Documentation 
Fit a first lasso regression and use Bayesian Information Criterion to determine '
adaptive weights (see lasso_bic
function for more details),
then run an adaptive lasso with this penalty weighting.
BIC is used for the adaptive lasso for variable selection.
Can deal with very large sparse data matrices.
Intended for binary reponse only (option family = "binomial"
is forced).
Depends on the glmnet
and relax.glmnet
function from the package
glmnet
.
adapt_bic(x, y, gamma = 1, maxp = 50, path = TRUE, betaPos = TRUE, ...)
x 
Input matrix, of dimension nobs x nvars. Each row is an observation
vector. Can be in sparse matrix format (inherit from class

y 
Binary response variable, numeric. 
gamma 
Tunning parameter to defined the penalty weights. See details below. Default is set to 1. 
maxp 
A limit on how many relaxed coefficients are allowed.
Default is 50, in 
path 
Since 
betaPos 
Should the covariates selected by the procedure be
positively associated with the outcome ? Default is 
... 
Other arguments that can be passed to 
The adaptive weight for a given covariate i is defined by
w_i = 1/β^{BIC}_i^γ
where β^{BIC}_i is the NON PENALIZED regression coefficient associated to covariate i obtained with lassobic.
An object with S3 class "adaptive"
.
aws 
Numeric vector of penalty weights derived from lassobic. Length equal to nvars. 
criterion 
Character, indicates which criterion is used with the
adaptive lasso for variable selection. For 
beta 
Numeric vector of regression coefficients in the adaptive lasso.
If 
selected_variables 
Character vector, names of variable(s) selected
with this adaptive approach.
If 
Emeline Courtois
Maintainer: Emeline Courtois
emeline.courtois@inserm.fr
set.seed(15) drugs < matrix(rbinom(100*20, 1, 0.2), nrow = 100, ncol = 20) colnames(drugs) < paste0("drugs",1:ncol(drugs)) ae < rbinom(100, 1, 0.3) ab < adapt_bic(x = drugs, y = ae, maxp = 50)