| splmm-package {splmm} | R Documentation |
Simultaneous Penalized Linear Mixed Effects Models
Description
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 Unpublished thesis.
Details
The DESCRIPTION file:
| Package: | splmm |
| Type: | Package |
| Title: | Simultaneous Penalized Linear Mixed Effects Models |
| Version: | 1.2.0 |
| Date: | 2024-06-12 |
| Authors@R: | c(person(given = "Luoying", family = "Yang", role = c("aut"), email = "lyang19@u.rochester.edu"), person(given = "Eli", family = "Sun", role = c("aut", "cre"), email = "eli_sun@urmc.rochester.edu"), person(given = "Tong Tong", family = "Wu", role = c("aut"), email = "tongtong_wu@urmc.rochester.edu")) |
| Maintainer: | Eli Sun <eli_sun@urmc.rochester.edu> |
| Description: | 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 Unpublished thesis. |
| License: | GPL-3 |
| Imports: | Rcpp (>= 1.0.1), emulator, miscTools, penalized, ggplot2, gridExtra, plot3D, MASS, progress, methods |
| LinkingTo: | Rcpp, RcppArmadillo |
| NeedsCompilation: | yes |
| Packaged: | 2024-06-05 18:57:45 UTC; elisunorig |
| Depends: | R (>= 3.5.0) |
| Author: | Luoying Yang [aut], Eli Sun [aut, cre], Tong Tong Wu [aut] |
| Repository: | CRAN |
| Date/Publication: | 2021-09-08 10:00:02 UTC |
Index of help topics:
cognitive Kenya School Lunch Intervention Cognitive
Dataset
plot.splmm Plot the tuning results of a 'splmm.tuning'
object
plot3D.splmm 3D Plot the tuning results of a "splmm.tuning"
object when tuning over both lambda 1 and
lambda 2 grids
print.splmm Print a short summary of a splmm object.
simulated_data Dataset simulated for toy example
splmm Function to fit linear mixed-effects model with
double penalty for fixed effects and random
effects
splmm-package Simultaneous Penalized Linear Mixed Effects
Models
splmmControl Options for the 'splmm' Algorithm
splmmTuning Tuning funtion of "splmm" object
summary.splmm Summarize an 'splmm' object
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.
Author(s)
Luoying Yang [aut], Eli Sun [aut, cre], Tong Tong Wu [aut]
Maintainer: Eli Sun <eli_sun@urmc.rochester.edu>
References
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
Examples
## 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)