mice.impute.logreg.boot

Logical vector of length <code>length(y)</code> indicating the 
the subset <code>y[ry]</code> of elements in <code>y</code> to which the imputation 
model is fitted. The <code>ry</code> generally distinguishes the observed 
(<code>TRUE</code>) and missing values (<code>FALSE</code>) in <code>y</code>.

Numeric design matrix with <code>length(y)</code> rows with predictors for 
<code>y</code>. Matrix <code>x</code> may have no missing values.

Logical vector of length <code>length(y)</code>. A <code>TRUE</code> value 
indicates locations in <code>y</code> for which imputations are created.

Imputes univariate missing data using logistic regression
by a bootstrapped logistic regression model.
The bootstrap method draws a simple bootstrap sample with replacement 
from the observed data <code>y[ry]</code> and <code>x[ry, ]</code>.

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

Multivariate Imputation by Chained Equations

Karin Groothuis-Oudshoorn

mice.impute.logreg.boot: Imputation by logistic regression using the bootstrap

Description

Usage

Arguments

Value

References

See Also