mice.impute.ri: Imputation by the random indicator method for nonignorable...

In mice: Multivariate Imputation by Chained Equations

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)

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

mice documentation built on June 7, 2023, 5:38 p.m.