Logistic Regression with Missing Covariates

Estimate parameters of logistic regression with missing data and perform model selection, using algorithm Stochastic Approximation EM.


misaem is an implementation of methodology which performs statistical inference for logistic regression model with missing data. This method is based on likelihood, including:

  1. Estimate the parameters of logistic regression by a stochastic approximation version of EM algorithm;
  2. Estimation of parameters' variance based one Louis formula;
  3. Model selection procedure based on BIC;
  4. Prediction on a test set which may contain missing values.

Installation of package

Now you can install the package misaem from CRAN.


Using the misaem package


  1. miss.saem contains the procedure of estimation for parameters, as well as their variance, and observed likelihood.
  2. model_selection aims at selecting a best model according to BIC.
  3. pred_saem performs prediction on a test set which may contain missing values.

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



Stochastic Approximation EM for Logistic regression with missing values (2018, Jiang W., Josse J., Lavielle M., Traumabase group)" arxiv link.


misaem 0.9.1

A minor release mainly fixing bugs and typos:

  • Fix a bug in model_selection, now it can correctly perform model selection if the full model is the best model.

  • In pred_saem, two methods for prediction of test set with missingness are provided.

  • Fix some typos in the vignettes. The length of codes now fits the page wide of html.

  • Delete unused Imports ‘magrittr’ in DESCRIPTION file.

  • Change the index of vignitte from 'SAEM' to 'misaem tutorial'.

  • Update README.md.

Reference manual

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0.9.1 by Wei Jiang, 3 months ago


Browse source code at https://github.com/cran/misaem

Authors: Wei Jiang [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports mvtnorm, stats, MASS

Suggests knitr, rmarkdown

See at CRAN