Imputes missing data in a categorical variable using polytomous regression
mice.impute.polr(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, ordered factors with more than two levels are imputed by
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.
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.