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Imputes univariate missing data using logistic regression.


mice.impute.logreg(y, ry, x, wy = NULL, ...)



Vector to be imputed


Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.


Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.


Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.


Other named arguments.


Vector with imputed data, same type as y, and of length sum(wy)


Imputation for binary response variables by the Bayesian logistic regression model (Rubin 1987, p. 169-170). The Bayesian method consists of the following steps:

  1. Fit a logit, and find (bhat, V(bhat))

  2. Draw BETA from N(bhat, V(bhat))

  3. Compute predicted scores for m.d., i.e. logit-1(X BETA)

  4. Compare the score to a random (0,1) deviate, and impute.

The method relies on the standard function. Warnings from are suppressed. Perfect prediction is handled by the data augmentation method.


Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. doi:10.18637/jss.v045.i03

Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets. Ph.D. Thesis, TNO Prevention and Health/Erasmus University Rotterdam. ISBN 90-74479-08-1.

Venables, W.N. & Ripley, B.D. (1997). Modern applied statistics with S-Plus (2nd ed). Springer, Berlin.

White, I., Daniel, R. and Royston, P (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational Statistics and Data Analysis, 54:22672275.


Stef van Buuren, Karin Groothuis-Oudshoorn