mice.impute.logreg {mice} | R Documentation |
Imputes univariate missing data using logistic regression.
mice.impute.logreg(y, ry, x, ...) mice.impute.logreg.boot(y, ry, x, ...)
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern of length |
x |
Matrix ( |
... |
Other named arguments. |
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:
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.
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.
imp |
A vector of length |
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000, 2011
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.