eMLEloglin: Fitting log-Linear Models in Sparse Contingency Tables

Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.

Author
Matthew Friedlander
Date of publication
2016-11-30 18:31:21
Maintainer
Matthew Friedlander <mattyf5@hotmail.com>
License
GPL (>= 2)
Version
1.0.1

View on CRAN

Man pages

eMLEloglin
Fitting log-linear models in sparse contingency tables.
facial_set
Finds the facial set
rochdale
The rochdale data

Files in this package

eMLEloglin
eMLEloglin/inst
eMLEloglin/inst/doc
eMLEloglin/inst/doc/User_manual.Rnw
eMLEloglin/inst/doc/User_manual.pdf
eMLEloglin/NAMESPACE
eMLEloglin/data
eMLEloglin/data/rochdale.rda
eMLEloglin/R
eMLEloglin/R/facial_set.R
eMLEloglin/vignettes
eMLEloglin/vignettes/User_manual.Rnw
eMLEloglin/vignettes/srcltx.sty
eMLEloglin/MD5
eMLEloglin/build
eMLEloglin/build/vignette.rds
eMLEloglin/DESCRIPTION
eMLEloglin/man
eMLEloglin/man/eMLEloglin.Rd
eMLEloglin/man/rochdale.Rd
eMLEloglin/man/facial_set.Rd