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GLMMadaptive

GLMMs with adaptive Gaussian quadrature

https://github.com/drizopoulos/glmmadaptive

Science Score: 13.0%

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Keywords

generalized-linear-mixed-models mixed-effects-models mixed-models
Last synced: 11 months ago · JSON representation

Repository

GLMMs with adaptive Gaussian quadrature

Basic Info
Statistics
  • Stars: 62
  • Watchers: 3
  • Forks: 14
  • Open Issues: 1
  • Releases: 3
Topics
generalized-linear-mixed-models mixed-effects-models mixed-models
Created about 8 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

GLMMadaptive: Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

R build status CRAN status Download counter Rdoc

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.

  • 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().

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: ```r 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 ```r 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 remotes package: ```r

install.packages("remotes")

remotes::install_github("drizopoulos/GLMMadaptive") ```

Hex-sticker courtesy of Greg Papageorgiou @gr_papageorgiou.

Owner

  • Name: Dimitris Rizopoulos
  • Login: drizopoulos
  • Kind: user
  • Location: Rotterdam
  • Company: Erasmus MC

Professor of Biostatistics Erasmus MC, Netherlands

GitHub Events

Total
  • Issues event: 1
  • Watch event: 2
  • Push event: 4
  • Fork event: 1
Last Year
  • Issues event: 1
  • Watch event: 2
  • Push event: 4
  • Fork event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 328
  • Total Committers: 2
  • Avg Commits per committer: 164.0
  • Development Distribution Score (DDS): 0.006
Past Year
  • Commits: 9
  • Committers: 2
  • Avg Commits per committer: 4.5
  • Development Distribution Score (DDS): 0.111
Top Committers
Name Email Commits
Dimitris Rizopoulos d****s@e****l 326
gpapageorgiou p****s@g****m 2
Committer Domains (Top 20 + Academic)
erasmusmc.nl: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 54
  • Total pull requests: 2
  • Average time to close issues: 13 days
  • Average time to close pull requests: 14 minutes
  • Total issue authors: 38
  • Total pull request authors: 1
  • Average comments per issue: 2.3
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: about 1 hour
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • strengejacke (9)
  • florianhartig (7)
  • moosterwegel (2)
  • wviechtb (2)
  • Mathematinho (1)
  • datasci-iopsy (1)
  • sfaridimehr (1)
  • andburch (1)
  • rvlenth (1)
  • elizabethnecka (1)
  • MarcelCSchmitt (1)
  • yuryzablotski (1)
  • Ivy-ops (1)
  • ajw11 (1)
  • TPERRIN684 (1)
Pull Request Authors
  • gpapageorgiou (2)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 4,895 last-month
  • Total docker downloads: 22,116
  • Total dependent packages: 10
  • Total dependent repositories: 26
  • Total versions: 18
  • Total maintainers: 1
cran.r-project.org: GLMMadaptive

Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

  • Versions: 18
  • Dependent Packages: 10
  • Dependent Repositories: 26
  • Downloads: 4,895 Last month
  • Docker Downloads: 22,116
Rankings
Dependent packages count: 5.3%
Dependent repos count: 5.4%
Stargazers count: 6.1%
Forks count: 6.3%
Downloads: 6.7%
Average: 9.1%
Docker downloads count: 24.8%
Maintainers (1)
d.rizopoulos@erasmusmc.nl
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • MASS * imports
  • matrixStats * imports
  • nlme * imports
  • parallel * imports
  • DHARMa * suggests
  • effects * suggests
  • emmeans * suggests
  • estimability * suggests
  • knitr * suggests
  • lattice * suggests
  • multcomp * suggests
  • optimParallel * suggests
  • pkgdown * suggests
  • rmarkdown * suggests
.github/workflows/R-CMD-check.yml actions
  • actions/cache v1 composite
  • actions/checkout v2 composite
  • actions/upload-artifact main composite
  • r-lib/actions/setup-pandoc v1 composite
  • r-lib/actions/setup-r v2 composite