splmm-package: Simultaneous Penalized Linear Mixed Effects Models
In splmm: Simultaneous Penalized Linear Mixed Effects Models

Description Details Author(s) References Examples

Contains functions that fit linear mixed-effects models for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation is based on the R package 'lmmlasso'. Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.

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Contains functions that fit linear mixed-effects models for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection.

Luoying Yang [aut, cre], Tong Tong Wu [aut]

Maintainer: Luoying Yang <lyang19@u.rochester.edu>

Luoying Yang PhD thesis

SCHELLDORFER, J., BUHLMANN, P. and DE GEER, S.V. (2011), Estimation for High-Dimensional Linear Mixed-Effects Models Using L1-Penalization. Scandinavian Journal of Statistics, 38: 197-214. doi:10.1111/j.1467-9469.2011.00740.x

## Use splmm on the Kenya school cognitive data set


data(cognitive)

x <- model.matrix(ravens ~schoolid+treatment+year+sex+age_at_time0
                  +height+weight+head_circ+ses+mom_read+mom_write
                  +mom_edu, cognitive)
z <- x

fit <- splmm(x=x,y=cognitive$ravens,z=z,grp=cognitive$id,lam1=0.1,
lam2=0.1,penalty.b="lasso", penalty.L="lasso")
summary(fit)