cv.glmmsel: Cross-validated generalised linear mixed model selection

In glmmsel: Generalised Linear Mixed Model Selection

Cross-validated generalised linear mixed model selection

Description

Fits the regularisation path for a sparse generalised linear mixed model and then cross-validates this path.

Usage

cv.glmmsel(
  x,
  y,
  cluster,
  family = c("gaussian", "binomial"),
  lambda = NULL,
  nfold = 10,
  folds = NULL,
  cv.loss = NULL,
  interpolate = TRUE,
  ...
)

Arguments

x	a predictor matrix
y	a response vector
cluster	a vector of length nrow(x) with the jth element identifying the cluster that the jth observation belongs to
family	the likelihood family to use; 'gaussian' for a continuous response or 'binomial' for a binary response
lambda	the regularisation parameter for the overlapping penalty on the fixed and random slopes
nfold	the number of cross-validation folds
folds	an optional vector of length nrow(x) with the jth entry identifying the fold that the jth observation belongs to
cv.loss	an optional cross-validation loss-function to use; should accept a vector of predicted values and a vector of actual values
interpolate	a logical indicating whether to interpolate the lambda sequence for the cross-validation fits
...	any other arguments for glmmsel()

Value

An object of class cv.glmmsel; a list with the following components:

cv.mean	a vector of cross-validation means
cv.sd	a vector of cross-validation standard errors
lambda	a vector of cross-validated regularisation parameters
lambda.min	the value of lambda minimising cv.mean
fit	the fit from running glmmsel() on the full data

Author(s)

Ryan Thompson <[e-mail]>

glmmsel documentation built on June 8, 2025, 9:39 p.m.