Apply a function across simulation results

Usage

apply_batch(inputs, fun, ..., id = "batch_id")

Arguments

inputs: List with at least one element, commonly data frames or simulation results. Element names label the batches; when inputs is unnamed the positions "1", "2", ... are used instead.
fun: Function applied to each element. When fun has a data argument, as lm() and glm() do, the element is passed as data; otherwise it is the first argument. fun returns a base data.frame, or a fitted model with named coefficients, which becomes a data frame with columns term and estimate. Every call must give the same columns, otherwise the run is an error.
...: Arguments passed to fun.
id: Single string naming the batch-label column, default "batch_id".

Value

A base data.frame stacking the fun outputs by row, with the batch label in a leading column named by id, so one row per row of each output.

Examples

inputs <- list(
  small = simulate_ttest(n_a = 20, n_b = 20, mean_a = 0, mean_b = 0.5, seed = 1),
  large = simulate_ttest(n_a = 60, n_b = 60, mean_a = 0, mean_b = 0.5, seed = 2)
)
apply_batch(inputs, fun = summary)
#>   batch_id variable     class observations missing unique       mean         sd
#> 1    small       id   integer           40       0     40 20.5000000 11.6904519
#> 2    small    group character           40       0      2         NA         NA
#> 3    small  outcome   numeric           40       0     40  0.3420262  0.8942982
#> 4    large       id   integer          120       0    120 60.5000000 34.7850543
#> 5    large    group character          120       0      2         NA         NA
#> 6    large  outcome   numeric          120       0    120  0.2819833  1.1451379
#>     minimum   maximum
#> 1  1.000000  40.00000
#> 2        NA        NA
#> 3 -2.214700   1.85868
#> 4  1.000000 120.00000
#> 5        NA        NA
#> 6 -2.451706   2.54804

# A model function is applied to each data set and tidied to coefficients.
datasets <- simulate_regression(seed = 1, batch = 3)
apply_batch(datasets, lm, formula = outcome ~ x1 + x2)
#>   batch_id        term   estimate
#> 1        1 (Intercept)  0.4871271
#> 2        1          x1  0.4291198
#> 3        1          x2 -0.4881249
#> 4        2 (Intercept) -0.5419140
#> 5        2          x1  0.4818146
#> 6        2          x2  0.5997644
#> 7        3 (Intercept) -0.7321067
#> 8        3          x1 -0.3789991
#> 9        3          x2  0.7836967