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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.


get_priors(x, ...)

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

# S3 method for dynamitefit
get_priors(x, ...)



[dynamiteformula or dynamitefit]
The model formula or an existing dynamitefit object. See dynamiteformula() and dynamite().




[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().


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.


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.


A data.frame containing the prior definitions.


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()


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