The R package
palasso implements the paired lasso.
Installing the current release from CRAN:
Installing the latest development version from GitHub:
We use glmnet for the standard lasso, and palasso for the paired lasso.
Loading and attaching the packages:
Attaching some data to reproduce the examples:
Data are available for \(n=30\) samples and \(p=50\) covariate pairs. The object
y contains the response (numeric vector of length \(n\)). The object
X contains the covariates (list of two numeric matrices, both with \(n\) rows and \(p\) columns).
The standard lasso is a good choice for exploiting either the first or the second covariate group:
But the paired lasso might be a better choice for exploiting both covariates groups at once:
In contrast to the standard lasso, the paired lasso accounts for the structure between the covariate groups.
Given a limited number of non-zero coefficients, we expect the paired lasso to outperform the standard lasso:
Standard methods are available for the paired lasso:
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