mice.impute.polyreg(y, ry, x, nnet.maxit=100, nnet.trace=FALSE, nnet.maxNWts=1500, ...)
mice.impute.polr(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 (for unordered factors) and
mice.impute.polr (for ordered factors). The function mice.impute.polyreg 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:
mice.impute.polyreg uses the function multinom()
from the nnet package. The function mice.impute.polr imputes for ordered categorical
response variables by the proportional odds logistic
regression (polr) model. The function repeatedly applies
logistic regression on the successive splits. The model is also
known as the cumulative link model.
The algorithm of mice.impute.polr uses the function polr()
from the MASS package.
In order to avoid bias due to perfect prediction, both algorithms augment the data according to the method of White, Daniel and Royston (2010).
The call to polr might fail, usually because the data are
very sparse. In that case, multinom is tried as a fallback,
and a record is written to the loggedEvents component of the
mids object.
mice: Multivariate Imputation by Chained Equations in R.
Journal of Statistical Software, 45(3), 1-67.
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