https://github.com/anestistouloumis/multgee
GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.
https://github.com/anestistouloumis/multgee
gee multinomial r
Last synced: about 1 month ago
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GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.
- Host: GitHub
- URL: https://github.com/anestistouloumis/multgee
- Owner: AnestisTouloumis
- Created: 2017-06-13T11:06:01.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-03-18T21:17:47.000Z (about 1 year ago)
- Last Synced: 2025-05-07T20:17:11.434Z (about 1 month ago)
- Topics: gee, multinomial, r
- Language: R
- Homepage: https://CRAN.R-project.org/package=multgee
- Size: 4.41 MB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
references:
- id: Touloumis2015
title: "R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses"
author:
- family: Touloumis
given: Anestis
container-title: Journal of Statistical Software
volume: 64
URL: 'https://www.jstatsoft.org/v064/i08'
issue: 8
page: 1-14
type: article-journal
issued:
year: 2015
- id: Touloumis2013
title: "GEE for Multinomial Responses Using a Local Odds Ratios Parameterization"
author:
- family: Touloumis
given: Anestis
- family: Agresti
given: Alan
- family: Kateri
given: Maria
container-title: Biometrics
volume: 69
URL: 'https://onlinelibrary.wiley.com/doi/10.1111/biom.12054/full'
issue: 3
page: 633--640
type: article-journal
issued:
year: 2013
csl: biometrics.csl
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
tidy = TRUE,
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# multgee: GEE Solver for Correlated Nominal or Ordinal Multinomial Responses
[![Github version](`r paste0("https://img.shields.io/badge/GitHub%20-", as.vector(read.dcf('DESCRIPTION')[, 'Version']),"-orange.svg")`)]("commits/master")
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[](http://www.repostatus.org/#active)
[](https://cran.r-project.org/package=multgee)
[](https://cranlogs.r-pkg.org/badges/grand-total/multgee)
[](https://cran.r-project.org/package=multgee)
## Installation
You can install the release version of `multgee`:
```{r eval=FALSE}
install.packages("multgee")
```
The source code for the release version of `multgee` is available on CRAN at:
- https://CRAN.R-project.org/package=multgee
Or you can install the development version of `multgee`:
```{r eval=FALSE}
# install.packages("devtools")
devtools::install_github("AnestisTouloumis/multgee")
```
The source code for the development version of `multgee` is available on github at:
- https://github.com/AnestisTouloumis/multgee
To use `multgee`, you should load the package as follows:
```{r}
library("multgee")
```
## Usage
This package provides a generalized estimating equations (GEE) solver for fitting marginal regression models with correlated nominal or ordinal multinomial responses based on a local odds ratios parameterization for the association structure [see @Touloumis2013].
There are two core functions to fit GEE models for correlated multinomial responses:
- `ordLORgee` for an ordinal response scale. Options for the marginal model include cumulative link models or an adjacent categories logit model,
- `nomLORgee` for a nominal response scale. Currently, the only option is a marginal baseline category logit model.
The main arguments in both functions are:
- an optional data frame (`data`),
- a model formula (`formula`),
- a cluster identifier variable (`id`),
- an optional vector that identifies the order of the observations within each cluster (`repeated`).
The association structure among the correlated multinomial responses is expressed via marginalized local odds ratios [@Touloumis2013]. The estimating procedure for the local odds ratios can be summarized as follows: For each level pair of the `repeated` variable, the available responses are aggregated across clusters to form a square marginalized contingency table. Treating these tables as independent, an RC-G(1) type model is fitted in order to estimate the marginalized local odds ratios. The `LORstr` argument determines the form of the marginalized local odds ratios structure. Since the general RC-G(1) model is closely related to the family of association models, one can instead fit an association model to each of the marginalized contingency tables by setting `LORem = "2way"` in the core functions.
There are also five useful utility functions:
- `confint` for obtaining Wald--type confidence intervals for the regression parameters using the standard errors of the sandwich (`method = "robust"`) or of the model--based (`method = "naive"`) covariance matrix. The default option is the sandwich covariance matrix (`method = "robust"`),
- `waldts` for assessing the goodness-of-fit of two nested GEE models based on a Wald test statistic,
- `vcov` for obtaining the sandwich (`method = "robust"`) or model--based (`method = "naive"`) covariance matrix of the regression parameters,
- `intrinsic.pars` for assessing whether the underlying association structure does not change dramatically across the level pairs of `repeated`,
- `gee_criteria` for reporting commonly used criteria to select variables and/or association structure for GEE models.
## Example
The following R code replicates the GEE analysis presented in @Touloumis2013.
```{r}
data("arthritis")
intrinsic.pars(y, arthritis, id, time, rscale = "ordinal")
```
The intrinsic parameters do not differ much. This suggests that the uniform local odds ratios structure might be a good approximation for the association pattern.
```{r}
fitord <- ordLORgee(formula = y ~ factor(time) + factor(trt) + factor(baseline),
data = arthritis, id = id, repeated = time)
summary(fitord)
```
The 95\% Wald confidence intervals for the regression parameters are
```{r}
confint(fitord)
```
To illustrate model comparison, consider another model with `age` and `sex` as additional covariates:
```{r}
fitord1 <- update(fitord, formula = . ~ . + age + factor(sex))
waldts(fitord, fitord1)
gee_criteria(fitord, fitord1)
```
According to the Wald test, there is no evidence of no difference between the two models. The QICu criterion suggest that `fitord` should be preferred over `fitord1`.
## Getting help
The statistical methods implemented in `multgee` are described in @Touloumis2013. A detailed description of the functionality of `multgee` can be found in @Touloumis2015. Note that an updated version of this paper also serves as a vignette:
```{r eval=FALSE}
browseVignettes("multgee")
```
## How to cite
```{r echo=FALSE, comment=""}
print(citation("multgee"), bibtex = TRUE)
```
# References