Prior_Setup: Setup Prior Objects
In knygren/glmbayes: Bayesian Generalized Linear Models (iid Samples)

Description Usage Arguments Value See Also Examples

Sets up the structure for the Prior mean and Variance Matrices using information from a classical model.

Prior_Setup(
  formula,
  data = NULL,
  subset = NULL,
  na.action = na.fail,
  drop.unused.levels = FALSE,
  xlev = NULL,
  ...
)

`formula`	a model `formula` or `terms` object or an R object.
`data`	a data.frame, list or environment (or object coercible by `as.data.frame` to a data.frame), containing the variables in `formula`. Neither a matrix nor an array will be accepted.
`subset`	a specification of the rows to be used: defaults to all rows. This can be any valid indexing vector (see `[.data.frame`) for the rows of `data` or if that is not supplied, a data frame made up of the variables used in `formula`.
`na.action`	how `NAs` are treated. The default is first, any `na.action` attribute of data, second a `na.action` setting of options, and third `na.fail` if that is unset. The `factory-fresh` default is `na.omit`. Another possible value is `NULL`.
`drop.unused.levels`	should factors have unused levels dropped? Defaults to `FALSE`.
`xlev`	a named list of character vectors giving the full set of levels to be assumed for each factor.
`...`	for `model.frame` methods, a mix of further arguments such as `data`, `na.action`, `subset` to pass to the default method. Any additional arguments (such as `offset` and `weights` or other named arguments) which reach the default method are used to create further columns in the model frame, with parenthesised names such as `"(offset)"`. For `get_all_vars`, further named columns to include in the model frame.

A list with items related to the prior.

`mu`	An initial version of the prior mean vector, populated with all zeros
`Sigma`	An initial version of the prior Variance-Covariance vector, populated as the diagonal identity matrix
`model`	The model frame from `object` if it exists
`x`	The design matrix from `object` if it exists

Other prior utility Functions: Prior_Check()

## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
## Step 1: Set up Prior
ps=Prior_Setup(counts ~ outcome + treatment)
mu=ps$mu
V=ps$Sigma
print(ps)