mice (version 3.16.0)

mice.impute.polyreg: Imputation of unordered data by polytomous regression

Description

Imputes missing data in a categorical variable using polytomous regression

Usage

mice.impute.polyreg(
  y,
  ry,
  x,
  wy = NULL,
  nnet.maxit = 100,
  nnet.trace = FALSE,
  nnet.MaxNWts = 1500,
  ...
)

Value

Vector with imputed data, same type as y, and of length sum(wy)

Arguments

y

Vector to be imputed

ry

Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.

x

Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.

wy

Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.

nnet.maxit

Tuning parameter for nnet().

nnet.trace

Tuning parameter for nnet().

nnet.MaxNWts

Tuning parameter for nnet().

...

Other named arguments.

Author

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010

Details

The function mice.impute.polyreg() imputes categorical response variables by the Bayesian polytomous regression model. See J.P.L. Brand (1999), Chapter 4, Appendix B.

By default, unordered factors with more than two levels are imputed by mice.impute.polyreg().

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

The algorithm of mice.impute.polyreg uses the function multinom() from the nnet 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).

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. tools:::Rd_expr_doi("10.18637/jss.v045.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.

See Also

mice, multinom, polr

Other univariate imputation functions: mice.impute.cart(), mice.impute.lasso.logreg(), mice.impute.lasso.norm(), mice.impute.lasso.select.logreg(), mice.impute.lasso.select.norm(), mice.impute.lda(), mice.impute.logreg.boot(), mice.impute.logreg(), mice.impute.mean(), mice.impute.midastouch(), mice.impute.mnar.logreg(), mice.impute.mpmm(), mice.impute.norm.boot(), mice.impute.norm.nob(), mice.impute.norm.predict(), mice.impute.norm(), mice.impute.pmm(), mice.impute.polr(), mice.impute.quadratic(), mice.impute.rf(), mice.impute.ri()