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.polr. 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, the algorithm 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