R: Imputation by the random indicator method for nonignorable...

mice.impute.ri {mice} | R Documentation

## Imputation by the random indicator method for nonignorable data

### Description

Imputes nonignorable missing data by the random indicator method.

### Usage

    mice.impute.ri(y, ry, x, wy = NULL, ri.maxit = 10, ...)

### 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.
`ri.maxit` | Number of inner iterations
`...` | Other named arguments.

### Details

The random indicator method estimates an offset between the distribution of the observed and missing data using an algorithm that iterates over the response and imputation models.

This routine assumes that the response model and imputation model have same predictors.

For an MNAR alternative see also `mice.impute.mnar.logreg`.

### Value

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

### Author(s)

Shahab Jolani (University of Utrecht) s.jolani@uu.nl

### References

Jolani, S. (2012). Dual Imputation Strategies for Analyzing Incomplete Data. Dissertation. University of Utrecht, Dec 7 2012.

### See Also

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.polyreg()`, `mice.impute.quadratic()`, `mice.impute.rf()`

[Package mice version 3.8.0 Index]