GLMMadaptive fits mixed effects models for grouped/clustered outcome variables for which the integral over the random effects in the definition of the marginal likelihood cannot be solved analytically. The package approximates these integrals using the adaptive Gauss-Hermite quadrature rule.
Multiple random effects terms can be included for the grouping factor (e.g., random intercepts, random linear slopes, random quadratic slopes), but currently only a single grouping factor is allowed.
The package contains a single model-fitting function named
mixed_model() with four required arguments,
fixed a formula for the fixed effects,
random a formula for the random effects,
family a family object specifying the type of response variable, and
data a data frame containing the variables in the previously mentioned formulas.
Negative binomial mixed models can be fitted using the
negative.binomial() family object.
Two-part/hurdle mixed models for semi-continuous normal data using the
hurdle.lognormal() family objects.
Users may also specify their own log-density function for the repeated measurements response variable, and the internal algorithms will take care of the optimization.
Calculates the marginalized coefficients using the idea of Hedeker et al. (2017) using function
Predictions with confidence interval for constructing effects plots are provided by function
y denote a grouped/clustered outcome,
g denote the grouping factor, and
x2 covariates. A mixed effects model with
y as outcome,
x2 as fixed effects, and random intercepts is fitted with the code:
data argument we provide the data frame
DF, which contains the aforementioned variables. In the family argument we specify the distribution of the grouped/clustered outcome conditional on the random effects. To include in the random-effects part intercepts and
x1, we update the call to
The development version of the package can be installed from GitHub using the devtools package:
and with vignettes
Hex-sticker courtesy of Greg Papageorgiou @gr_papageorgiou.