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.
Methods for standard generics are provided, i.e.,
coef(), fixef(), ranef(),
vcov(), logLik(), summary(),
anova(), confint(), fitted(),
residuals(), predict(), and
simulate().
Negative binomial mixed models can be fitted using the
negative.binomial() family object.
Zero-inflated Poisson and negative binomial models using the
zi.poisson() and zi.negative.binomial() family
objects.
Hurdle Poisson and negative binomial models using the
hurdle.poisson() and
hurdle.negative.binomial() family objects.
Zero-inflated binomial models using the
zi.binomial() family objects.
Two-part/hurdle mixed models for semi-continuous normal data
using the hurdle.lognormal() family object.
Mixed models for censored normal data using the
censored.normal() family object.
Continuation ratio mixed models for ordinal data using functions
cr_setup() and cr_marg_probs().
Beta and hurdle Beta mixed effects models using
beta.fam() and hurdle.beta.fam() family
objects.
Gamma mixed effects models using the Gamma() or
Gamma.fam() family object.
Linear mixed effects models with right and left censored data
using the censored.normal() family object.
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
marginal_coefs().
Predictions with confidence interval for constructing effects
plots are provided by function effectPlotData().
Let y denote a grouped/clustered outcome, g
denote the grouping factor, and x1 and x2
covariates. A mixed effects model with y as outcome,
x1 and x2 as fixed effects, and random
intercepts is fitted with the code:
fm <- mixed_model(fixed = y ~ x1 + x2, random = ~ 1 | g, data = DF,
family = poisson())
summary(fm)In the 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 mixed_model() as
gm <- mixed_model(fixed = y ~ x1 + x2, random = ~ x1 | g, data = DF,
family = poisson())
summary(gm)The development version of the package can be installed from GitHub using the devtools package:
devtools::install_github("drizopoulos/GLMMadaptive")and with vignettes
devtools::install_github("drizopoulos/GLMMadaptive", build_opts = NULL)Hex-sticker courtesy of Greg Papageorgiou @gr_papageorgiou.