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
#>    response missing group   time   patient         age biomarker
#>       <dbl> <lgl>   <chr>   <chr>  <chr>         <dbl>     <dbl>
#>  1       NA FALSE   group_1 time_1 patient_001 -0.0499    -1.30 
#>  2       NA FALSE   group_1 time_2 patient_001 -0.0499    -1.30 
#>  3       NA FALSE   group_1 time_3 patient_001 -0.0499    -1.30 
#>  4       NA FALSE   group_1 time_4 patient_001 -0.0499    -1.30 
#>  5       NA FALSE   group_1 time_1 patient_002  1.75       0.592
#>  6       NA FALSE   group_1 time_2 patient_002  1.75       0.592
#>  7       NA FALSE   group_1 time_3 patient_002  1.75       0.592
#>  8       NA FALSE   group_1 time_4 patient_002  1.75       0.592
#>  9       NA FALSE   group_1 time_1 patient_003  0.400      1.05 
#> 10       NA FALSE   group_1 time_2 patient_003  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
#>    response missing group   time   patient     biomarker1 biomarker2
#>       <dbl> <lgl>   <chr>   <chr>  <chr>            <dbl>      <dbl>
#>  1       NA FALSE   group_1 time_1 patient_001      1154.      1066.
#>  2       NA FALSE   group_1 time_2 patient_001      1154.      1066.
#>  3       NA FALSE   group_1 time_3 patient_001      1154.      1066.
#>  4       NA FALSE   group_1 time_4 patient_001      1154.      1066.
#>  5       NA FALSE   group_1 time_1 patient_002      1108.      1084.
#>  6       NA FALSE   group_1 time_2 patient_002      1108.      1084.
#>  7       NA FALSE   group_1 time_3 patient_002      1108.      1084.
#>  8       NA FALSE   group_1 time_4 patient_002      1108.      1084.
#>  9       NA FALSE   group_1 time_1 patient_003       714.      1111.
#> 10       NA FALSE   group_1 time_2 patient_003       714.      1111.
#> # ℹ 790 more rows