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mice (version 1.14)

impute.logreg: Elementary Imputation Method: Logistic Regression

Description

Imputes univariate missing data using logistic regression.

Usage

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.

Value

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

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 impute.
The method relies on the standard glm.fit function.

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, impute.logreg2