cisl  R Documentation 
Implementation of CISL and the stability selection according to subsampling options.
cisl(
x,
y,
r = 4,
nB = 100,
dfmax = 50,
nlambda = 250,
nMin = 0,
replace = TRUE,
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. 
r 
Number of control in the CISL sampling. Default is 4. See details below for other implementations. 
nB 
Number of subsamples. Default is 100. 
dfmax 
Corresponds to the maximum size of the models visited with the lasso (E in the paper). Default is 50. 
nlambda 
Number of lambda values as is 
nMin 
Minimum number of events for a covariate to be considered.
Default is 0, all the covariates from 
replace 
Should sampling be with replacement? Default is TRUE. 
betaPos 
If 
ncore 
The number of calcul units used for parallel computing.
This has to be set to 1 if the 
CISL is a variation of the stability method adapted to characteristics of pharmacovigilance databases.
Tunning r = 4
and replace = TRUE
are used to implement our CISL sampling.
For instance, r = NULL
and replace = FALSE
can be used to
implement the n \over 2
sampling in Stability Selection.
An object with S3 class "cisl"
.
prob 
Matrix of dimension nvars x 
q05 
5 
q10 
10 
q15 
15 
q20 
20 
Ismail Ahmed
Ahmed, I., Pariente, A., & TubertBitter, P. (2018). "Classimbalanced subsampling lasso algorithm for discovering adverse drug reactions". Statistical Methods in Medical Research. 27(3), 785–797, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0962280216643116")}
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)
lcisl < cisl(x = drugs, y = ae, nB = 50)
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