mice.impute.logreg {mice}R Documentation

Elementary Imputation Method: 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.

Details

Imputation for binary response variables by the Bayesian logistic regression model. See Rubin (1987, p. 169-170) for a description of the method. The 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 mice.impute.

The method relies on the standard glm.fit function.

Value

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

Note

An alternative is mice.impute.logreg2.

Author(s)

Stef van Buuren, Karin Oudshoorn, 2000

References

Van Buuren, S. & Oudshoorn, C.G.M. (2000). Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Report PG/VGZ/00.038, TNO Prevention and Health, Leiden.

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.

See Also

mice, glm, glm.fit, mice.impute.logreg2


[Package mice version 1.15 Index]