lasso_perm  R Documentation 
Performed K lasso logistic regression with K different permuted version of the outcome.
For earch of the lasso regression, the \lambda_max
(i.e. the smaller
\lambda
such as all penalized regression coefficients are shrunk to zero)
is obtained.
The median value of these K \lambda_max
is used to for variable selection
in the lasso regression with the nonpermuted outcome.
Depends on the glmnet
function from the package glmnet
.
lasso_perm(x, y, K = 20, keep = NULL, betaPos = TRUE, ncore = 1, ...)
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. 
K 
Number of permutations of 
keep 
Do some variables of 
betaPos 
Should the covariates selected by the procedure be positively
associated with the outcome ? Default is 
ncore 
The number of calcul units used for parallel computing. Default is 1, no parallelization is implemented. 
... 
Other arguments that can be passed to 
The selected \lambda
with this approach is defined as the closest
\lambda
from the median value of the K \lambda_max
obtained
with permutation of the outcome.
An object with S3 class "log.lasso"
.
beta 
Numeric vector of regression coefficients in the lasso
In 
selected_variables 
Character vector, names of variable(s) selected with the
lassoperm approach.
If 
Emeline Courtois
Maintainer: Emeline Courtois
emeline.courtois@inserm.fr
Sabourin, J. A., Valdar, W., & Nobel, A. B. (2015). "A permutation approach for selecting the penalty parameter in penalized model selection". Biometrics. 71(4), 1185–1194, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/biom.12359")}
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)
lp < lasso_perm(x = drugs, y = ae, K = 10)
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