summary.brms_mmrm_archetype: Summarize an informative prior archetype.

In brms.mmrm: Bayesian MMRMs using 'brms'

Summarize an informative prior archetype.

Description

For an informative prior archetype, show the transformation from model parameters to marginal means.

Usage

## S3 method for class 'brms_mmrm_archetype'
summary(object, message = TRUE, ...)

Arguments

object	The informative prior archetype to summarize.
message	TRUE to print an informative message about the archetype and invisibly return a character vector of equations. FALSE to forgo verbose messages and non-invisibly return the equations.
...	Not used, but required for S3 methods that inherit from the base generic summary().

Value

Return a character vector with linear equations that map model parameters to marginal means. If the message argument is TRUE (default) then this character vector is returned invisibly and a verbose description of the equations is printed.

Examples

data <- brm_simulate_outline(
  n_group = 2,
  n_patient = 100,
  n_time = 4,
  rate_dropout = 0,
  rate_lapse = 0
) |>
  dplyr::mutate(response = rnorm(n = dplyr::n())) |>
  brm_data_change() |>
  brm_simulate_continuous(names = c("biomarker1", "biomarker2")) |>
  brm_simulate_categorical(
    names = c("status1", "status2"),
    levels = c("present", "absent")
  )
dplyr::select(
  data,
  group,
  time,
  patient,
  starts_with("biomarker"),
  starts_with("status")
)
archetype <- brm_archetype_successive_cells(data)
equations <- summary(archetype)
print(equations)
summary(archetype, message = FALSE)

brms.mmrm documentation built on Oct. 3, 2024, 1:08 a.m.