buildbam: Use 'buildmer' to fit big generalized additive models using...

In cvoeten/buildmer: Stepwise Elimination and Term Reordering for Mixed-Effects Regression

Use buildmer to fit big generalized additive models using bam from package mgcv

Description

Use buildmer to fit big generalized additive models using bam from package mgcv

Usage

buildbam(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)

Arguments

formula	See the general documentation under buildmer-package
data	See the general documentation under buildmer-package
family	See the general documentation under buildmer-package
buildmerControl	Control arguments for buildmer — see the general documentation under buildmerControl

Details

To work around an issue in bam, you must make sure that your data do not contain a variable named 'intercept'.

lme4 random effects are supported: they will be automatically converted using re2mgcv.

As bam uses PQL, only crit='F' and crit='deviance' (note that the latter is not a formal test) are supported for non-Gaussian errors.

See Also

buildmer-package

Examples

library(buildmer)
model <- buildbam(f1 ~ s(timepoint,by=following) + s(participant,by=following,bs='re') +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

cvoeten/buildmer documentation built on March 3, 2023, 3:25 p.m.