Variable Selection in Linear Mixed Models for SNP Data
Simulation Scenario from Bhatnagar et al. (2018+) ggmix paper
Fit Linear Mixed Model with Lasso or Group Lasso Regularization
Constructor functions for the different ggmix objects
Generalised Information Criterion
Functions related to eta parameter used in optim and kkt checks
Check of KKT Conditions for Linear Mixed Model
Estimation of Lambda Sequence for Linear Mixed Model with Lasso Penalt...
Estimation of Linear Mixed Model with Lasso Penalty
Estimation of Log-likelihood for Linear Mixed Model with Lasso Penalty
Plot Method for ggmix_fit object
Plot the Generalised Information Criteria curve produced by gic
Make predictions from a ggmix_fit object
Make predictions from a ggmix_gic object
Print Method for Objects of Class ggmix_fit
Extract Random Effects
Estimation of Sigma2 for Linear Mixed Model with Lasso Penalty
Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, <DOI:10.1371/journal.pgen.1008766>.