mice.impute.polyreg: Imputation by Polytomous Regression
Description
Imputes missing data in a categorical variable using polytomous regression
Usage
mice.impute.polyreg(y, ry, x)
Arguments
y
Incomplete data vector of length n
ry
Vector of missing data pattern (FALSE=missing, TRUE=observed)
x
Matrix (n x p) of complete covariates.
Value
A vector of length nmis with imputations.
Details
Imputation for categorical response variables by the Bayesian
polytomous regression model. See J.P.L. Brand (1999), Chapter 4,
Appendix B.
The method consists of the following steps:
Fit categorical response as a multinomial model
Compute predicted categories
Add appropriate noise to predictions.
This algorithm uses the function multinom() from the libraries nnet
(Venables and Ripley).
References
Van Buuren, S., Groothuis-Oudshoorn, K. (2009)
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.
Dissertation. Rotterdam: Erasmus University.
Venables, W.N. & Ripley, B.D. (1997). Modern applied statistics with S-Plus (2nd ed). Springer, Berlin.