| palasso {palasso} | R Documentation |
Paired lasso
Description
The function palasso fits the paired lasso.
Use this function if you have paired covariates
and want a sparse model.
Usage
palasso(y = y, X = X, max = 10, ...)
Arguments
y |
response:
vector of length |
X |
covariates:
list of matrices,
each with |
max |
maximum number of non-zero coefficients:
positive numeric, or |
... |
Details
Let x denote one entry of the list X. See glmnet
for alternative specifications of y and x. Among the further
arguments, family must equal "gaussian", "binomial",
"poisson", or "cox", and penalty.factor must not be
used.
Hidden arguments:
Deactivate adaptive lasso by setting adaptive to FALSE,
activate standard lasso by setting standard to TRUE,
and activate shrinkage by setting shrink to TRUE.
Value
This function returns an object of class palasso.
Available methods include
predict,
coef,
weights,
fitted,
residuals,
deviance,
logLik,
and summary.
References
A Rauschenberger, I Ciocanea-Teodorescu, RX Menezes, MA Jonker, and MA van de Wiel (2020). "Sparse classification with paired covariates." Advances in Data Analysis and Classification. 14:571-588. doi:10.1007/s11634-019-00375-6, pdf, armin.rauschenberger@uni.lu
Examples
set.seed(1)
n <- 50; p <- 20
y <- rbinom(n=n,size=1,prob=0.5)
X <- lapply(1:2,function(x) matrix(rnorm(n*p),nrow=n,ncol=p))
object <- palasso(y=y,X=X,family="binomial") # adaptive=TRUE,standard=FALSE
names(object)