Deprecated on 2023-09-01 (version 0.0.2.9001). Use
brm_simulate_simple() instead.
Usage
brm_simulate(
n_group = 2L,
n_patient = 100L,
n_time = 4L,
hyper_beta = 1,
hyper_sigma = 1,
hyper_correlation = 1
)Arguments
- n_group
Positive integer of length 1, number of treatment groups.
- n_patient
Positive integer of length 1, number of patients per treatment group.
- n_time
Positive integer of length 1, number of discrete time points (e.g. scheduled study visits) per patient.
- hyper_beta
Positive numeric of length 1, hyperparameter. Prior standard deviation of the fixed effect parameters.
- hyper_sigma
Positive numeric of length 1, hyperparameter. Uniform prior upper bound of the time-specific residual standard deviation parameters.
- hyper_correlation
Positive numeric of length 1, hyperparameter. LKJ shape parameter of the correlation matrix among repeated measures within each patient.
Value
A list of three objects:
data: A tidy dataset with one row per patient per discrete time point and columns for the response and covariates.model_matrix: A matrix with one row per row ofdataand columns that represent levels of the covariates.parameters: A named list of parameter values sampled from the prior.
Examples
set.seed(0L)
simulation <- suppressWarnings(brm_simulate())
simulation$data
#> # A tibble: 800 × 4
#> response group patient time
#> <dbl> <chr> <chr> <chr>
#> 1 1.30 group 1 patient 1 time 1
#> 2 2.52 group 1 patient 1 time 2
#> 3 2.63 group 1 patient 1 time 3
#> 4 1.98 group 1 patient 1 time 4
#> 5 1.22 group 1 patient 2 time 1
#> 6 2.63 group 1 patient 2 time 2
#> 7 2.38 group 1 patient 2 time 3
#> 8 2.52 group 1 patient 2 time 4
#> 9 1.32 group 1 patient 3 time 1
#> 10 2.63 group 1 patient 3 time 2
#> # ℹ 790 more rows