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glmfitmiss (version 2.1.0)

Fitting GLMs with Missing Data in Both Responses and Covariates

Description

Fits generalized linear models (GLMs) when there is missing data in both the response and categorical covariates. The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) , Maiti and Pradhan (2009) , Maity, Pradhan, and Das (2019) for further methodological details.

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Install

install.packages('glmfitmiss')

Monthly Downloads

192

Version

2.1.0

License

MIT + file LICENSE

Maintainer

Vivek Pradhan

Last Published

April 22nd, 2025

Functions in glmfitmiss (2.1.0)

meningitis- Meningococcal Disease Data with missing data in the response variable

Fitting binary regression with missing categorical covariates using new likelihood based method

Fitting binary regression with missing categorical covariates using new likelihood based method that does not require EM algorithm

ibrahim data -- Ibrahim (1990) JASA

meningitis60ymis

meningitis60ymis- Meningococcal Disease Data with missing data in the response variable

formula generation

metastmelanoma - metastatic melanoma trial data

simulateMissDfYorX

Simulate missing covariate or missing responses data based on an input covariate data

theta_back_2_data

This function performs data augmentation on the provided dataset.

Simulated Test Data -- testyxm

sixcitydata -- A very well published Six city data published in many articles including Ware et al (1984), Ibrahim and Lipsitz (1996). Also avaialble in LogXact User Manual. The dataset is a longitudinal study of the health effects of air pollution (ware et al., 1984).

simulateCovariateData

Simulate data with independent categorical covariates

Simulate data based on an input covariate data

Fitting binary regression with missing responses that are nonignorable based on Ibrahim and Lipsitz (1996)

Fitting binary regression model with missing responses based on Ibrahim and Lipsitz (1996)

EST data -- Eastern Cooperative Oncology Group clinical trials, EST 2282

Fitting binary regression with missing categorical covariates using likelihood based method

emBinRegMixedMAR

Fits binary regression models with both nonignorable missing responses and missing categorical covariates.

Fitting generalized linear models with Incomplete data

Fitting binary regression with missing categorical covariates using Expectation-Maximisation (EM) based method

EST data -- Eastern Cooperative Oncology Group clinical trials, EST 2282

dataAugmentation

Data Augmentation Function

checkCharacterVariablesInFormula

Function to check if any character variables exist in a formula and show an error

incontinence- incontinence Data taken from brlrmr pacakge

glmfitmiss-package

glmfitmiss: Fitting Binary Regression Models with Missing Data

felinedata -- Chlamydial Infection in Cats