mice.impute.ri {mice}R Documentation

Imputation by the random indicator method for nonignorable data

Description

Imputes univariate missing data using the random indicator method. This method estimates an offset between the distribution of the observed and missing data using an algorithm that iterates over the response model and the imputation model.

Usage

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

Arguments

y

Incomplete data vector of length n

ry

Vector of missing data pattern (FALSE=missing, TRUE=observed)

x

Matrix (n x p) of complete covariates.

ri.maxit

Number of inner iterations

...

Other named arguments passed down to .norm.draw()

Value

A vector of length nmis with imputations.

Author(s)

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

References

Jolani, S. (2012). Dual Imputation Strategies for Analyzing Incomplete Data. Disseration. University of Utrecht, Dec 7 2012. http://igitur-archive.library.uu.nl/dissertations/2012-1120-200602/Jolani.pdf


[Package mice version 2.22 Index]