mice.impute.ri

Incomplete data vector of length <code>n</code>

Vector of missing data pattern (<code>FALSE</code>=missing,
<code>TRUE</code>=observed)

Matrix (<code>n</code> x <code>p</code>) of complete covariates.

ri.maxit

Other named arguments passed down to <code>.norm.draw()</code>


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.


datagen

Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm. Each variable has its own imputation
model. Built-in imputation models are provided for continuous data
(predictive mean matching, normal), binary data (logistic regression),
unordered categorical data (polytomous logistic regression) and ordered
categorical data (proportional odds). MICE can also impute continuous
two-level data (normal model, pan, second-level variables). Passive
imputation can be used to maintain consistency between variables. Various
diagnostic plots are available to inspect the quality of the imputations.

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

Description

Usage

Arguments

Value

References