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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mice.impute.norm.R
\name{norm.draw}
\alias{norm.draw}
\alias{.norm.draw}
\title{Draws values of beta and sigma by Bayesian linear regression}
\usage{
norm.draw(y, ry, x, rank.adjust = TRUE, ...)
.norm.draw(y, ry, x, rank.adjust = TRUE, ...)
}
\arguments{
\item{y}{Incomplete data vector of length \code{n}}
\item{ry}{Vector of missing data pattern (\code{FALSE}=missing,
\code{TRUE}=observed)}
\item{x}{Matrix (\code{n} x \code{p}) of complete covariates.}
\item{rank.adjust}{Argument that specifies whether \code{NA}'s in the
coefficients need to be set to zero. Only relevant when \code{ls.meth = "qr"}
AND the predictor matrix is rank-deficient.}
\item{...}{Other named arguments.}
}
\value{
A \code{list} containing components \code{coef} (least squares estimate),
\code{beta} (drawn regression weights) and \code{sigma} (drawn value of the
residual standard deviation).
}
\description{
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.
}
\references{
Rubin, D.B. (1987). \emph{Multiple imputation for nonresponse in surveys}. New York: Wiley.
}
\author{
Gerko Vink, 2018, for this version, based on earlier versions written
by Stef van Buuren, Karin Groothuis-Oudshoorn, 2017
}
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