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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 for more
information.
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 \(\lambda\), with different values yielding
different sparsity levels. The cv.glmmsel() function
provides a convenient method for automatically cross-validating \(\lambda\).
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 0See the package vignette or reference manual.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.