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.

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

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.

A `list`

containing components `coef`

(least squares estimate),
`beta`

(drawn regression weights) and `sigma`

(drawn value of the
residual standard deviation).

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