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mice (version 2.9)

mice.impute.logreg: Multiple Imputation by Logistic Regression

Description

Imputes univariate missing data using logistic regression.

Usage

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

  • impA 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) or bootstrap logistic regression model. 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. The bootstrap method draws a bootstrap sample from y[ry] and x[ry,]. 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, forthcoming. http://www.stefvanbuuren.nl/publications/MICE in R - Draft.pdf 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