Linear Regression and Logistic Regression with Missing Covariates

Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) .

misaem package

Introduction

misaem is a package to perform linear regression and logistic regression with missing data, under MCAR (Missing completely at random) and MAR (Missing at random) mechanisms. The covariates are assumed to be continuous variables. The methodology implemented is based on maximization of the observed likelihood using EM-types of algorithms. The package includes:

1. Parameters estimation.
2. Estimation of standard deviation for estimated parameters.
3. Model selection procedure based on BIC.

Installation of package

Now you can install the package misaem from CRAN.

install.packages("misaem")

Using the misaem package

Basically,

1. miss.glm is the main function performing logistic regression with missing values.
2. miss.lm is the main function performing linear regression with missing values.

For more details, You can find the vignette, which illustrate the basic and further usage of misaem package:

library(misaem)
vignette('misaem')

Reference

Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction (2020, Jiang W., Josse J., Lavielle M., TraumaBase Group), Computational Statistics & Data Analysis.

Code development

The misaem package was initially developed by Wei Jiang at github.com/wjiang94/misaem. Subsequent development was carried out by Imke Mayer for version 1.0.1 at github.com/imkemayer/misaem, and by Christophe Muller for version 1.1.0 at github.com/ChristopheMuller/misaem.