cta-package {cta}

cta: Contingency Table Analysis Based on ML Fitting of MPH Models

Description

Contingency table analysis is performed based on maximum likelihood (ML) fitting of multinomial-Poisson homogeneous (MPH) models (Lang, 2004) and homogeneous linear predictor (HLP) models (Lang, 2005). Objects computed include model goodness-of-fit statistics; likelihood-based (cell- and link-specific) residuals; and cell probability and expected count estimates along with standard errors. This package can also compute test-inversion–e.g. Wald, profile likelihood, score, power-divergence–confidence intervals for contingency table estimands, when table probabilities are potentially subject to equality constraints. See Lang (2008) and Zhu (2020) for test-inversion intervals.

Details

Please call the following two R functions in this cta package.

mph.fit: Computes maximum likelihood estimates and fit statistics for MPH and HLP models for contingency tables.

ci.table: Constructs test-inversion approximate confidence intervals for estimands in contingency tables with or without equality constraints.

Author(s)

Joseph B. Lang, Qiansheng Zhu

References

Lang, J. B. (2004) Multinomial-Poisson homogeneous models for contingency tables, Annals of Statistics, 32, 340–383.

Lang, J. B. (2005) Homogeneous linear predictor models for contingency tables, Journal of the American Statistical Association, 100, 121–134.

Lang, J. B. (2008) Score and profile likelihood confidence intervals for contingency table parameters, Statistics in Medicine, 27, 5975–5990.

Zhu, Q. (2020) "On improved confidence intervals for parameters of discrete distributions." PhD dissertation, University of Iowa.