mice.impute.ri: 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.
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