Imputes nonignorable missing data by the random indicator method.

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

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.

Vector with imputed data, same type as `y`

, and of length
`sum(wy)`

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

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

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.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`