Extracts the priors used in the dynamite model as a data frame. You
can then alter the priors by changing the contents of the `prior`

column and
supplying this data frame to `dynamite`

function using the argument
`priors`

. See vignettes for details.

## Usage

```
get_priors(x, ...)
# S3 method for dynamiteformula
get_priors(x, data, time, group = NULL, ...)
# S3 method for dynamitefit
get_priors(x, ...)
```

## Arguments

- x
[

`dynamiteformula`

or`dynamitefit`

]

The model formula or an existing`dynamitefit`

object. See`dynamiteformula()`

and`dynamite()`

.- ...
Ignored.

- data
[

`data.frame`

,`tibble::tibble`

, or`data.table::data.table`

]

The data that contains the variables in the model in long format. Supported column types are`integer`

,`logical`

,`double`

, and`factor`

. Columns of type`character`

will be converted to factors. Unused factor levels will be dropped. The`data`

can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input`data`

is converted to channel specific matrix representations via`stats::model.matrix.lm()`

.- time
[

`character(1)`

]

A column name of`data`

that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.- group
[

`character(1)`

]

A column name of`data`

that denotes the unique groups or`NULL`

corresponding to a scenario without any groups. If`group`

is`NULL`

, a new column`.group`

is created with constant value`1L`

is created indicating that all observations belong to the same group. In case of name conflicts with`data`

, see the`group_var`

element of the return object to get the column name of the new variable.

## Note

Only the `prior`

column of the output should be altered when defining
the user-defined priors for the `dynamite`

.

## See also

Model fitting
`dynamice()`

,
`dynamite()`

,
`update.dynamitefit()`

## Examples

```
data.table::setDTthreads(1) # For CRAN
d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
get_priors(obs(y ~ x, family = "gaussian"),
data = d, time = "time", group = "id"
)
#> parameter response prior type category
#> 1 alpha_y y normal(2.4, 2) alpha
#> 2 beta_y_x y normal(0, 0.66) beta
#> 3 sigma_y y exponential(1) sigma
```