mice (version 2.25)

mice.impute.logreg: Imputation by logistic regression

Description

Imputes univariate missing data using logistic regression.

Usage

mice.impute.logreg(y, ry, x, ...)

Arguments

y

Incomplete data vector of length n

ry

Vector of missing data pattern of length n (FALSE=missing, TRUE=observed)

x

Matrix (n x p) of complete covariates.

...

Other named arguments.

Value

A vector of length nmis with imputations (0 or 1).

Details

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 glm.fit function. Warnings from glm.fit are suppressed. Perfect prediction is handled by the data augmentation method.

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. http://www.jstatsoft.org/v45/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.

See Also

mice, glm, glm.fit