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 |
x |
Numeric design matrix with |
wy |
Logical vector of length |
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()