mice.impute.polyreg(y, ry, x, nnet.maxit = 100, nnet.trace = FALSE, nnet.maxNWts = 1500, ...)nFALSE=missing,
TRUE=observed)n x p) of complete covariates.nnet().nnet().nnet().nmis with imputations.
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:
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).
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.
mice, multinom,
polr