Imputes univariate missing data using logistic regression.
Usage
imp <- impute.logreg2(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:
Fit a logit, and find (bhat, V(bhat))
Draw BETA from N(bhat, V(bhat))
Compute predicted scores for m.d., i.e. logit-1(X BETA)
Compare the score to a random (0,1) deviate, and impute.
This method uses direct minimization of the likelihood function
by means of V&R function logitreg (V&R, 2nd ed, p. 293).
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.
Venables, W.N. & Ripley, B.D. (1997). Modern applied statistics with S-Plus (2nd ed). Springer, Berlin.