Append simulated continuous covariates

Simulate and append non-time-varying continuous covariates to an existing brm_data() dataset.

Usage

brm_simulate_continuous(data, names, mean = 0, sd = 1)

Arguments

data

Classed tibble as from brm_data() or brm_simulate_outline().

names

Character vector with the names of the new covariates to simulate and append. Names must all be unique and must not already be column names of data.

mean

Numeric of length 1, mean of the normal distribution for simulating each covariate.

sd

Positive numeric of length 1, standard deviation of the normal distribution for simulating each covariate.

Value

A classed tibble, like from brm_data() or brm_simulate_outline(), but with new numeric covariate columns and with the names of the new covariates appended to the brm_covariates attribute.

Details

Each covariate is a new column of the dataset with one independent random univariate normal draw for each patient. All covariates simulated this way are independent of everything else in the data, including other covariates (to the extent that the random number generators in R work as intended).

See also

Other simulation: brm_simulate_categorical(), brm_simulate_outline(), brm_simulate_prior(), brm_simulate_simple()

Examples

data <- brm_simulate_outline()
brm_simulate_continuous(
  data = data,
  names = c("age", "biomarker")
)
#> # A tibble: 800 × 7
#>    patient     time   group   missing response     age biomarker
#>    <chr>       <chr>  <chr>   <lgl>      <dbl>   <dbl>     <dbl>
#>  1 patient_001 time_1 group_1 FALSE         NA -0.0499    -1.30 
#>  2 patient_001 time_2 group_1 FALSE         NA -0.0499    -1.30 
#>  3 patient_001 time_3 group_1 FALSE         NA -0.0499    -1.30 
#>  4 patient_001 time_4 group_1 FALSE         NA -0.0499    -1.30 
#>  5 patient_002 time_1 group_1 FALSE         NA  1.75       0.592
#>  6 patient_002 time_2 group_1 FALSE         NA  1.75       0.592
#>  7 patient_002 time_3 group_1 FALSE         NA  1.75       0.592
#>  8 patient_002 time_4 group_1 FALSE         NA  1.75       0.592
#>  9 patient_003 time_1 group_1 FALSE         NA  0.400      1.05 
#> 10 patient_003 time_2 group_1 FALSE         NA  0.400      1.05 
#> # ℹ 790 more rows
brm_simulate_continuous(
  data = data,
  names = c("biomarker1", "biomarker2"),
  mean = 1000,
  sd = 100
)
#> # A tibble: 800 × 7
#>    patient     time   group   missing response biomarker1 biomarker2
#>    <chr>       <chr>  <chr>   <lgl>      <dbl>      <dbl>      <dbl>
#>  1 patient_001 time_1 group_1 FALSE         NA      1154.      1066.
#>  2 patient_001 time_2 group_1 FALSE         NA      1154.      1066.
#>  3 patient_001 time_3 group_1 FALSE         NA      1154.      1066.
#>  4 patient_001 time_4 group_1 FALSE         NA      1154.      1066.
#>  5 patient_002 time_1 group_1 FALSE         NA      1108.      1084.
#>  6 patient_002 time_2 group_1 FALSE         NA      1108.      1084.
#>  7 patient_002 time_3 group_1 FALSE         NA      1108.      1084.
#>  8 patient_002 time_4 group_1 FALSE         NA      1108.      1084.
#>  9 patient_003 time_1 group_1 FALSE         NA       714.      1111.
#> 10 patient_003 time_2 group_1 FALSE         NA       714.      1111.
#> # ℹ 790 more rows