Imputes nonignorable missing data by the random indicator method.
mice.impute.ri(y, ry, x, wy = NULL, ri.maxit = 10, ...)Vector with imputed data, same type as y, and of length
sum(wy)
Vector to be imputed
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.
Numeric design matrix with length(y) rows with predictors for
y. Matrix x may have no missing values.
Logical vector of length length(y). A TRUE value
indicates locations in y for which imputations are created.
Number of inner iterations
Other named arguments.
Shahab Jolani (University of Utrecht)
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.
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.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()