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.
Two-part/hurdle mixed models for semi-continuous normal data using the
hurdle.lognormal()
family objects.
Beta and hurdle Beta mixed effects models using beta.fam()
and hurdle.beta.fam()
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 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.
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