mice.impute.polyreg {mice}R Documentation

Imputation by Polytomous Regression

Description

Imputes missing data in a categorical variable using polytomous regression

Usage

mice.impute.polyreg(y, ry, x)

Arguments

y Incomplete data vector of length n
ry Vector of missing data pattern (FALSE=missing, TRUE=observed)
x Matrix (n x p) of complete covariates.

Details

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:

  1. Fit categorical response as a multinomial model
  2. Compute predicted categories
  3. Add appropriate noise to predictions.

This algorithm uses the function multinom() from the libraries nnet (Venables and Ripley).

Value

A vector of length nmis with imputations.

Author(s)

Stef van Buuren, Karin Groohuis-Oudshoorn, 2000

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2009) MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming. http://www.stefvanbuuren.nl/publications/MICE in R - Draft.pdf

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.

Venables, W.N. & Ripley, B.D. (1997). Modern applied statistics with S-Plus (2nd ed). Springer, Berlin.

See Also

mice, multinom


[Package mice version 2.2 Index]