adapt_univ  R Documentation 
Compute oddratios between each covariate of x
and y
then derived
adaptive weights to incorporate in an adaptive lasso.
BIC or crossvalidation could either be used for the adaptive lasso for variable selection.
Two options for implementing crossvalidation for the adaptive lasso are possible through the type_cv
parameter (see bellow).
Can deal with very large sparse data matrices.
Intended for binary reponse only (option family = "binomial"
is forced).
The crossvalidation criterion used is deviance.
Depends on the glmnet
and relax.glmnet
function from the package
glmnet
.
adapt_univ(
x,
y,
gamma = 1,
criterion = "bic",
maxp = 50,
path = TRUE,
nfolds = 5,
foldid = NULL,
type_cv = "proper",
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. 
criterion 
Character, indicates which criterion is used with the adaptive lasso for variable selection. Could be either "bic" or "cv". Default is "bic" 
maxp 
Used only if 
path 
Used only if 
nfolds 
Used only if 
foldid 
Used only if 
type_cv 
Used only if 
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/\beta^{univ}_i^\gamma
where
\beta^{univ}_i = log(OR_i)
, with
OR_i
is the oddratio associated to covariate i
with the outcome.
An object with S3 class "adaptive"
.
aws 
Numeric vector of penalty weights derived from oddsratios. Length equal to nvars. 
criterion 
Character, same as input. Could be either "bic" or "cv". 
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)
au < adapt_univ(x = drugs, y = ae, criterion ="cv", nfolds = 3)
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