mice.impute.polyreg {mice}R Documentation

Elementary Imputation Method: 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: begin{enumerate}

  • Fit categorical response as a multinomial model
  • Compute predicted categories
  • Add appropriate noise to predictions. end{enumerate} This algorithm uses the function multinom from the libraries nnet and MASS (Venables and Ripley).

    Value

    A vector of length nmis with imputations.

    Author(s)

    Stef van Buuren, Karin Oudshoorn, 2000

    References

    Van Buuren, S. & Oudshoorn, C.G.M. (2000). Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Report PG/VGZ/00.038, TNO Prevention and Health, Leiden.

    Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets. Ph.D. Thesis, TNO Prevention and Health/Erasmus University Rotterdam. ISBN 90-74479-08-1.

    See Also

    mice, multinom


    [Package mice version 1.15 Index]