Change the center of a nuisance variable of an informative prior archetype.
Arguments
- data
An informative prior archetype data frame output from
brm_archetype_cells()or similar.- nuisance
Character of length 1, name of the nuisance column in the data to shift the center.
- center
Numeric of length 1, value of the center to shift the column in
nuisance. The affected column in the returned archetype data frame will look as if it were centered by this value to begin with.
Details
By "centering vector y at scalar x", we mean taking
the difference z = y - x. If x is the mean, then mean(z) is
0. Informative prior archetypes center nuisance variables
at their means so the parameters can be interpreted correctly
for setting informative priors. This is appropriate most of the time,
but sometimes it is better to center a column at a pre-specified
scientifically meaningful fixed number. If you want a nuisance column
to be centered at a fixed value other than its mean,
use brm_recenter_nuisance() to shift the center. This function
can handle any nuisance variable
For a nuisance variable which interacts baseline response with discrete time and/or the subgroup, the center applies to baseline within the cell of the data that the interaction applies to. The rest of the column stays at zero.
Recentering a nuisance variable shifts the reference level of the model,
which changes the interpretation of the marginal means from
brm_marginal_draws(). It is your responsibility to interpret the
reference level and the resulting marginal means appropriately.
Please recenter with brm_recenter_nuisance() rather than modifying
the column directly, so the recorded center stays in sync with the
column. brm_transform_marginal() prints an informative message
(once per session) if it detects a nuisance column that was recentered
some other way.
Examples
set.seed(0L)
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")
)
archetype <- brm_archetype_cells(data)
mean(archetype$nuisance_biomarker1) # after original centering
#> [1] -2.088553e-17
center <- mean(data$biomarker1)
center # original center, before the centering from brm_archetype_cells()
#> [1] -0.05507499
attr(archetype$nuisance_biomarker1, "brm_center") # original center
#> [1] -0.05507499
max(abs((data$biomarker1 - center) - archetype$nuisance_biomarker1))
#> [1] 0
# Re-center nuisance_biomarker1 at 0.75.
archetype <- brm_recenter_nuisance(
data = archetype,
nuisance = "nuisance_biomarker1",
center = 0.75
)
attr(archetype$nuisance_biomarker1, "brm_center") # new center
#> [1] 0.75
mean(archetype$nuisance_biomarker1) # no longer equal to the center
#> [1] -0.805075
# nuisance_biomarker1 is now as though we centered it at 0.75.
max(abs((data$biomarker1 - 0.75) - archetype$nuisance_biomarker1))
#> [1] 2.220446e-16