multgee-package: A GEE Solver For Correlated Nominal Or Ordinal Multinomial...

In AnestisTouloumis/multgee: GEE Solver for Correlated Nominal or Ordinal Multinomial Responses

A GEE Solver For Correlated Nominal Or Ordinal Multinomial Responses

Description

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.

GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.

Details

The package contains two functions that fit GEE models for correlated multinomial responses; ordLORgee for an ordinal response scale and nomLORgee for a nominal response scale.

The main arguments in both functions are: (i) an optional data frame (data), (ii) a model formula (formula), (iii) a cluster identifier variable (id) and (iv) an optional vector that identifies the order of the observations within each cluster (repeated).

Options for the marginal model in the function ordLORgee include cumulative link models or an adjacent categories logit model. A marginal baseline category logit model is offered in the function nomLORgee. For the form of the linear predictor in these models, see the Details sections in nomLORgee and ordLORgee.

The association structure among the correlated multinomial responses is expressed via marginalized local odds ratios (Touloumis et al., 2013). 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 (Becker and Clogg, 1989) 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 (Goodman, 1985), one can instead fit an association model to each of the marginalized contingency tables by setting LORem="2way".

If the underlying association pattern does not change dramatically across the level pairs of repeated then parsimonious marginalized local odds ratios should sufficiently approximate the true underlying association structure. To assess the underlying association structure, one might use the utility function intrinsic.pars.

Instead of estimating the local odds ratios structure, a user-defined structure can be provided by setting LORstr="fixed". In this case, the utility function matrixLOR is useful in constructing the required LORterm argument.

The function waldts provides a goodness-of-fit test between two nested GEE models based on a Wald test statistic.

Author(s)

Anestis Touloumis Maintainer: Anestis Touloumis <[e-mail]>

Maintainer: Anestis Touloumis [e-mail] (ORCID)

References

Becker, M. and Clogg, C. (1989) Analysis of sets of two-way contingency tables using association models. Journal of the American Statistical Association 84, 142–151.

Goodman, L. (1985) The analysis of cross-classified data having ordered and/or unordered categories: Association models, correlation models, and asymmetry models for contingency tables with or without missing entries. The Annals of Statistics 13, 10–69.

Touloumis, A., Agresti, A. and Kateri, M. (2013) GEE for multinomial responses using a local odds ratios parameterization. Biometrics 69, 633–640.

Touloumis, A. (2015) R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses. Journal of Statistical Software 64, 1–14.

See Also

Useful links:

• https://github.com/AnestisTouloumis/multgee

• Report bugs at https://github.com/AnestisTouloumis/multgee/issues

AnestisTouloumis/multgee documentation built on March 19, 2024, 9:55 p.m.