# GLMMadaptive v0.7-0

Monthly downloads

## Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

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)
<doi:10.1080/10618600.1995.10474663>.

## Readme

# GLMMadaptive: Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

## Description

**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.

## 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()`

,`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.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.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()`

.

## Basic Use

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)
```

## 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_opts = NULL)
```

Hex-sticker courtesy of Greg Papageorgiou @gr_papageorgiou.

## Functions in GLMMadaptive

Name | Description | |

effectPlotData | Predicted Values for Effects Plots | |

GLMMadaptive | Generalized Linear Mixed Models using Adaptive Gaussian Quadrature | |

Continuation Ratio Set-Up | Functions to Set-Up Data for a Continuation Ratio Mixed Model | |

negative.binomial | Family function for Negative Binomial Mixed Models | |

mixed_model | Generalized Linear Mixed Effects Models | |

Extra Family Objects | Family functions for Student's-t, Beta, Zero-Inflated and Hurdle Poisson and Negative Binomial, Hurdle Log-Normal, and Hurdle Beta Mixed Models | |

MixMod Methods | Various Methods for Standard Generics | |

marginal_coefs | Marginal Coefficients from Generalized Linear Mixed Models | |

scoring_rules | Proper Scoring Rules for Categorical Data | |

No Results! |

## Vignettes of GLMMadaptive

Name | ||

Custom_Models.Rmd | ||

GLMMadaptive_basics.Rmd | ||

Methods_MixMod.Rmd | ||

No Results! |

## Last month downloads

## Details

Date | 2020-06-25 |

BugReports | https://github.com/drizopoulos/GLMMadaptive/issues |

Encoding | UTF-8 |

LazyLoad | yes |

LazyData | yes |

License | GPL (>= 3) |

URL | https://drizopoulos.github.io/GLMMadaptive/, https://github.com/drizopoulos/GLMMadaptive |

VignetteBuilder | knitr |

RoxygenNote | 6.1.1 |

NeedsCompilation | no |

Packaged | 2020-06-25 06:59:25 UTC; drizo |

Repository | CRAN |

Date/Publication | 2020-06-25 08:30:03 UTC |

suggests | DHARMa , effects , emmeans , estimability , knitr , lattice , multcomp , optimParallel , pkgdown , rmarkdown |

imports | MASS , matrixStats , nlme , parallel |

Contributors |

#### Include our badge in your README

```
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```