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Label a prior with levels in the data.

Source: R/brm_prior_label.R
brm_prior_label.Rd

Label an informative prior for a parameter using a collection of levels in the data.

Usage

brm_prior_label(label = NULL, code, group, subgroup = NULL, time)

Arguments

label

A tibble with the prior labeling scheme so far, with one row per model parameter and columns for the Stan code, treatment group, subgroup, and discrete time point of each parameter.

code

Character of length 1, Stan code for the prior. Could be a string like "normal(1, 2.2)". The full set of priors is given in the Stan Function Reference at https://mc-stan.org/docs/functions-reference/ in the "Unbounded Continuous Distributions" section. See the documentation brms::set_prior() for more details.

group

Value of length 1, level of the treatment group column in the data to label the prior. The treatment group column is the one you identified with the group argument of brm_data().

subgroup

Value of length 1, level of the subgroup column in the data to label the prior. The subgroup column is the one you identified with the subgroup argument of brm_data(), if applicable. Not every dataset has a subgroup variable. If yours does not, please either ignore this argument or set it to NULL.

time

Value of length 1, level of the discrete time column in the data to label the prior. The discrete time column is the one you identified with the time argument of brm_data().

Value

A tibble with one row per model parameter and columns for the Stan code, treatment group, subgroup, and discrete time point of each parameter. You can supply this tibble to the label argument of brm_prior_archetype().

Prior labeling

Informative prior archetypes use a labeling scheme to assign priors to fixed effects. How it works:

1. First, assign the prior of each parameter a collection
  of labels from the data. This can be done manually or with
  successive calls to [brm_prior_label()].
2. Supply the labeling scheme to [brm_prior_archetype()].
  [brm_prior_archetype()] uses attributes of the archetype
  to map labeled priors to their rightful parameters in the model.

For informative prior archetypes, this process is much more convenient and robust than manually calling brms::set_prior(). However, it requires an understanding of how the labels of the priors map to parameters in the model. This mapping varies from archetype to archetype, and it is documented in the help pages of archetype-specific functions such as brm_archetype_successive_cells().

See also

Other priors: brm_prior_archetype(), brm_prior_simple(), brm_prior_template()

Examples

set.seed(0L)
data <- brm_simulate_outline(
  n_group = 2,
  n_patient = 100,
  n_time = 3,
  rate_dropout = 0,
  rate_lapse = 0
) |>
  dplyr::mutate(response = rnorm(n = dplyr::n())) |>
  brm_simulate_continuous(names = c("biomarker1", "biomarker2")) |>
  brm_simulate_categorical(
    names = c("status1", "status2"),
    levels = c("present", "absent")
  )
archetype <- brm_archetype_successive_cells(data)
dplyr::distinct(data, group, time)
#> # A tibble: 6 × 2
#>   group   time  
#>   <chr>   <chr> 
#> 1 group_1 time_1
#> 2 group_1 time_2
#> 3 group_1 time_3
#> 4 group_2 time_1
#> 5 group_2 time_2
#> 6 group_2 time_3
label <- NULL |>
  brm_prior_label("normal(1, 1)", group = "group_1", time = "time_1") |>
  brm_prior_label("normal(1, 2)", group = "group_1", time = "time_2") |>
  brm_prior_label("normal(1, 3)", group = "group_1", time = "time_3") |>
  brm_prior_label("normal(2, 1)", group = "group_2", time = "time_1") |>
  brm_prior_label("normal(2, 2)", group = "group_2", time = "time_2") |>
  brm_prior_label("normal(2, 3)", group = "group_2", time = "time_3")
label
#> # A tibble: 6 × 3
#>   code         group   time  
#>   <chr>        <chr>   <chr> 
#> 1 normal(1, 1) group_1 time_1
#> 2 normal(1, 2) group_1 time_2
#> 3 normal(1, 3) group_1 time_3
#> 4 normal(2, 1) group_2 time_1
#> 5 normal(2, 2) group_2 time_2
#> 6 normal(2, 3) group_2 time_3