R: Draws values of beta and sigma by Bayesian linear regression

norm.draw {mice} | R Documentation

## Draws values of beta and sigma by Bayesian linear regression

### 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.

### Usage

    norm.draw(y, ry, x, rank.adjust = TRUE, ...)

    .norm.draw(y, ry, x, rank.adjust = TRUE, ...)

### Arguments

`y` | Incomplete data vector of length `n`
`ry` | Vector of missing data pattern (`FALSE`=missing, `TRUE`=observed)
`x` | Matrix (`n` x `p`) of complete covariates.
`rank.adjust` | Argument that specifies whether `NA`'s in the coefficients need to be set to zero. Only relevant when `ls.meth = "qr"` AND the predictor matrix is rank-deficient.
`...` | Other named arguments.

### Value

A `list` containing components `coef` (least squares estimate), `beta` (drawn regression weights) and `sigma` (drawn value of the residual standard deviation).

### Author(s)

Gerko Vink, 2018, for this version, based on earlier versions written by Stef van Buuren, Karin Groothuis-Oudshoorn, 2017

### References

Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.

[Package mice version 3.12.0 Index]