.norm.draw

norm.draw

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.

A small numerical value specifying the size of the ridge used. 
The default value <code>ridge = 1e-05</code> represents a compromise between stability
and unbiasedness. Decrease <code>ridge</code> if the data contain many junk variables.
Increase <code>ridge</code> for highly collinear data.

ridge

This function draws random values of beta and sigma under the Bayesian 
linear regression model as described in Rubin (1987, p. 167). This function
can be called by user-specified imputation functions.

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

norm.draw: Draws values of beta and sigma by Bayesian linear regression

Description

Usage

Arguments

Value

References