Fits generalized linear mixed models for a single grouping factor under
maximum likelihood approximating the integrals over the random effects with an
adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995)
This repository contains the source files for the R package GLMMadaptive. This package 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
fixed a formula for the fixed effects,
random a formula for the
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.,
Negative binomial mixed models can be fitted using the
Zero-inflated Poisson and negative binomial models using the
zi.negative.binomial() family objects.
Hurdle Poisson and negative binomial models using the
hurdle.negative.binomial() family objects.
Two-part/hurdle mixed models for semi-continuous normal data using the
hurdle.lognormal() family objects.
Beta and hurdle Beta mixed effects models using
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
Predictions with confidence interval for constructing effects plots are provided by
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:
fm <- mixed_model(fixed = y ~ x1 + x2, random = ~ 1 | g, data = DF,family = poisson())summary(fm)
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
x1, we update the call to
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:
and with vignettes
devtools::install_github("drizopoulos/GLMMadaptive", build_opts = NULL)
Hex-sticker courtesy of Greg Papageorgiou @gr_papageorgiou.
predict() method now works for zero-inflated and hurdle models.
Hurdle Beta mixed effects models are now available using the
The new function
scoring_rules() calculates proper scoring rule for subject-specific
predictions from mixed models for categorical data.
Added support for the emmeans package.
Hurdle Poisson and negative binomial models are now implemented using the family objects
added S3 methods for the
model.matrix() generics in order to
work with the multcomp package.
A new vignette illustrating multiple comparisons with the multcomp package.
simulate() have gained the logical argument
sandwich to invoke the use of robust/sandwich standard errors in the calculations.
Zero-inflated Poisson and negative binomial models are now implemented using the family objects
zi.negative.binomial(), respectively. In addition, taking into advantage of the fact that users can specify their own log density functions for the outcome, two-part / hurdle model can also be implemented.
A new vignette illustrates how the zero-inflated models can be fitted.
predict() method is now fully available. It calculates predictions, and standard errors for models returned by
mixed_model() at three levels:
"mean subject": only the fixed effects part corresponding to predictions for the average subject (but not population averaged predictions in case of nonlinear link functions).
"marginal": predictions using the marginalized coefficients that correspond to population averaged predictions.
"subject specific": predictions at the subject level. These can be also calculated for subjects not originally in the dataset (i.e., estimates of the random effects are internally obtained).
simulate() method is available to simulate data from fitted mixed models. This can be used for instance to perform replication / posterior predictive checks.