.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. 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.

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

Description

Usage

Arguments

Value

References