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.

Install the latest version of this package by entering the following in R:
install.packages("eMLEloglin")
AuthorMatthew Friedlander
Date of publication2016-11-30 18:31:21
MaintainerMatthew Friedlander <mattyf5@hotmail.com>
LicenseGPL (>= 2)
Version1.0.1

View on CRAN

Files

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

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