| cmm-package {cmm} | R Documentation |
Categorical Marginal Models
Description
Quite extensive package for maximum likelihood estimation and weighted least squares estimation of categorical marginal models (CMMs; e.g., Bergsma & Rudas, 2002; Bergsma, Croon and Hagenaars, 2009
Details
| Package: | cmm |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2023-08-08 |
| License: | GPL (>= 2) |
The package contains principal functions for analyzing marginal models for categorical data. All functions are illustrated using examples from the book Marginal Models for Dependent, Clustered, and Longitudunal Categorical Data (Bergsma, Croon, & Hagenaars, 2009).
The package contains the following functions
ConstraintMatrix
DesignMatrix
DirectSum
JoinModels
MarginalMatrix
MarginalModelFit
ModelStatistics
SampleStatistics
SpecifyCoefficient
The package contains the following data sets
Antisemitism
BodySatisfaction
ClarenceThomas
DutchConcern
DutchPolitics
ErieCounty
EVS
GSS93
LaborParticipation
MarihuanaAlcohol
NES
NKPS
NKPS2
Smoking
As of version 1.0, the option of maximum augmented empirical likelihood estimation (MAEL) estimation (Van der Ark et al., 2023), which is particularly useful for large data set.
The following functions were added:
Margins
RecordsToFrequencies;
the following data set was added acl;
and the following function was updated: MarginalMatrix.
Author(s)
Wicher P. Bergsma Maintainer: L. Andries van der Ark L.A.vanderArk@uva.nl.
References
Bergsma, W. P. (1997). Marginal models for categorical data. Tilburg, The Netherlands: Tilburg University Press. http://stats.lse.ac.uk/bergsma/pdf/bergsma_phdthesis.pdf
Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudunal categorical data. Berlin: Springer. doi:10.1007/b12532
Bergsma, W. P.& Rudas T. (2002). Marginal models for categorical data. The Annals of Statistics, 30, 1, 140-159. doi:10.1214/aos/1015362188
Van der Ark, L. A., Bergsma, W. P., & Koopman L. (2023) Maximum augmented empirical likelihood estimation of categorical marginal models for large sparse contingency tables. Paper submitted for publication.