glmfitmiss-package {glmfitmiss}R Documentation

glmfitmiss: Fitting Binary Regression Models with Missing Data

Description

The glmfitmiss package provides functions for fitting binary regression models in the presence of missing data in both response variable level and covariate levels. The package includes likelihood-based methods, primarily based on the EM algorithm by Ibrahim (1990) for handling missing data mechanisms. Bias-reducing adjusted score approaches introduced by Firth (1993) are also incorporated in all the supported methods.

Details

This package enhances the accuracy of binary regression modeling in the presence of missing data by incorporating Ibrahim (1990) EM algorithm and Firth (1993) bias-reducing adjusted score methods.

The main functions in this package are:

The other functions and data included in this package are

Author(s)

Maintainer: Vivek Pradhan vpradhan2009@gmail.com

Authors:

References

Firth, D. (1993). Bias reduction of maximum likelihood estimates, Biometrika, 80, 27-38. doi:10.2307/2336755.

Ibrahim, J. G. (1990). Incomplete data in generalized linear models. Journal of the American Statistical Association 85, 765–769.

Ibrahim, J. G., and Lipsitz, S. R. (1996). Parameter Estimation from Incomplete Data in Binomial Regression when the Missing Data Mechanism is Nonignorable, Biometrics, 52, 1071–1078.

Kosmidis, I., Firth, D. (2021). Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models. Biometrika, 108, 71-82. doi:10.1093/biomet/asaa052.

Louis, T. A. (1982). Finding the observed information when using the EM algorithm. Proceedings of the Royal Statistical Society, Ser B, 44, 226-233.

Maiti, T., Pradhan, V. (2009). Bias reduction and a solution of separation of logistic regression with missing covariates. Biometrics, 65, 1262-1269.

Pradhan, V., Nychka, D. and Bandyopadhyay, S. (2025). Beyond the Odds: Fitting Logistic Regression with Missing Data in Small Samples (submitted).

Pradhan, V., Nychka, D., and Bandyopadhyay, S. (2025). Addressing Missing Responses and Categorical Covariates in Binary Regression Modeling: An Integrated Framework (to be submitted).

Pradhan, V., Nychka, D., and Bandyopadhyay, S. (2025). Bridging Gaps in Logistic Regression: Tackling Missing Categorical Covariates with a New Likelihood Method (to be submitted).

Pradhan, V., Nychka, D., and Bandyopadhyay, S. (2025). glmFitMiss: Binary Regression with Missing Data in R (to be submitted).

See Also

emBinRegMAR, emBinRegMixedMAR, logRegMAR, meningitis, emforbeta, meningitis60ymis, emyxmiss, est, metastmelanoma, simulateCovariateData, est45, simulateData, felinedata, sixcitydata, ibrahim, testyxm, llkmiss


[Package glmfitmiss version 2.1.0 Index]