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>

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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 as described in Van Buuren and
Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. 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.

Stef van Buuren

mice

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

Description

Usage

Arguments

Value

References