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 class 'dynamiteformula'
get_priors(x, data, time, group = NULL, ...)
# S3 method for class 'dynamitefit'
get_priors(x, ...)
Arguments
- x
[
dynamiteformula
ordynamitefit
]
The model formula or an existingdynamitefit
object. Seedynamiteformula()
anddynamite()
.- ...
Ignored.
- data
[
data.frame
,tibble::tibble
, ordata.table::data.table
]
The data that contains the variables in the model in long format. Supported column types areinteger
,logical
,double
, andfactor
. Columns of typecharacter
will be converted to factors. Unused factor levels will be dropped. Thedata
can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Inputdata
is converted to channel specific matrix representations viastats::model.matrix.lm()
.- time
[
character(1)
]
A column name ofdata
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 ofdata
that denotes the unique groups orNULL
corresponding to a scenario without any groups. Ifgroup
isNULL
, a new column.group
is created with constant value1L
is created indicating that all observations belong to the same group. In case of name conflicts withdata
, see thegroup_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 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(0.74, 2.4) alpha
#> 2 beta_y_x y normal(0, 0.78) beta
#> 3 sigma_y y exponential(0.84) sigma