glmmsel

Generalised linear mixed model selection

https://github.com/ryan-thompson/glmmsel

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Generalised linear mixed model selection

README.Rmd

---
output: github_document
---

```{r setup, include = F}
knitr::opts_chunk$set(echo = T)
```

# glmmsel

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## Overview

An R package for generalised linear mixed model (GLMM) selection. `glmmsel` uses an $\ell_0$ regulariser to simultaneously select fixed and random effects. A hierarchical constraint is included that a random effect cannot be selected unless its corresponding fixed effect is also selected. Gaussian and binomial families are currently supported. See [this paper](https://arxiv.org/abs/2506.20425) for more information.

## Installation

To install the latest version from GitHub, run the following code:
``` {r, eval = F}
devtools::install_github('ryan-thompson/glmmsel')
```

## Usage

The `glmmsel()` function fits a sparse GLMM over a sequence of the regularisation parameter $\lambda$, with different values yielding different sparsity levels. The `cv.glmmsel()` function provides a convenient method for automatically cross-validating $\lambda$.
```{r, example} 
library(glmmsel)

# Generate some clustered data
n <- 100 # Number of observations
m <- 4 # Number of clusters
p <- 5 # Number of predictors
s.fix <- 2 # Number of nonzero fixed effects
s.rand <- 1 # Number of nonzero random effects
x <- matrix(rnorm(n * p), n, p) # Predictor matrix
beta <- c(rep(1, s.fix), rep(0, p - s.fix)) # True fixed effects
u <- cbind(matrix(rnorm(m * s.rand), m, s.rand), matrix(0, m, p - s.rand)) # True random effects
cluster <- sample(1:m, n, replace = TRUE) # Cluster labels
xb <- rowSums(x * sweep(u, 2, beta, '+')[cluster, ]) # x %*% (beta + u) matrix
y <- rnorm(n, xb) # Response vector

# Fit the ℓ0 regularisation path
fit <- glmmsel(x, y, cluster)
coef(fit, lambda = 10)

# Cross-validate the ℓ0 regularisation path
fit <- cv.glmmsel(x, y, cluster)
coef(fit)
```

## Documentation

See the package [vignette](https://CRAN.R-project.org/package=glmmsel/vignettes/vignette.html) or [reference manual](https://CRAN.R-project.org/package=glmmsel/glmmsel.pdf).

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