Imputes missing data in a categorical variable using polytomous regression
mice.impute.polyreg(y, ry, x, nnet.maxit = 100, nnet.trace = FALSE,
nnet.MaxNWts = 1500, ...)Incomplete data vector of length n
Vector of missing data pattern (FALSE=missing,
TRUE=observed)
Matrix (n x p) of complete covariates.
Tuning parameter for nnet().
Tuning parameter for nnet().
Tuning parameter for nnet().
Other named arguments.
A vector of length nmis with imputations.
By default, unordered factors with more than two levels are imputed by
mice.impute.polyreg(). 'The function mice.impute.polyreg()
imputes 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.
The algorithm of mice.impute.polyreg uses the function
multinom() from the nnet package.
In order to avoid bias due to perfect prediction, the algorithm augment the data according to the method of White, Daniel and Royston (2010).
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. Dissertation. Rotterdam: Erasmus University.
White, I.R., Daniel, R. Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational Statistics and Data Analysis, 54, 2267-2275.
Venables, W.N. & Ripley, B.D. (2002). Modern applied statistics with S-Plus (4th ed). Springer, Berlin.