An R package for generalised linear mixed model (GLMM) selection.
glmmsel uses an
To install the latest version from GitHub, run the following code:
devtools::install_github('ryan-thompson/glmmsel')The glmmsel() function fits a sparse GLMM over a sequence of the
regularisation parameter cv.glmmsel() function provides a
convenient method for automatically cross-validating
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)## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.12410477 1.128917 1.053438 0 0 0
## [2,] 0.15875835 1.128917 1.053438 0 0 0
## [3,] -0.01088924 1.128917 1.053438 0 0 0
## [4,] 0.09670996 1.128917 1.053438 0 0 0
# Cross-validate the ℓ0 regularisation path
fit <- cv.glmmsel(x, y, cluster)
coef(fit)## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.12410477 1.128917 1.053438 0 0 0
## [2,] 0.15875835 1.128917 1.053438 0 0 0
## [3,] -0.01088924 1.128917 1.053438 0 0 0
## [4,] 0.09670996 1.128917 1.053438 0 0 0
See the package vignette or reference manual.