GLMMadaptive: Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

Description

This repository contains the source files for the R package GLMMadaptive. This package fits mixed effects models for grouped / repeated measurements data 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.

Basic Features

• 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(), and predict().

• Negative binomial mixed models can be fitted using the negative.binomial() 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().

Installation

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_vignettes = TRUE)