R: Imputation of unordered data by polytomous regression

mice.impute.polyreg {mice} | R Documentation

## 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,
      ...
    )

### 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.

### 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).

### Value

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

### Author(s)

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

### References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate Imputation by Chained Equations in `R`. Journal of Statistical Software, 45(3), 1-67. https://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.

### See Also

`mice`, `multinom`, `polr`

Other univariate imputation functions: `mice.impute.cart()`, `mice.impute.lda()`, `mice.impute.logreg.boot()`, `mice.impute.logreg()`, `mice.impute.mean()`, `mice.impute.midastouch()`, `mice.impute.mnar.logreg()`, `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()`

[Package mice version 3.13.0 Index]