Given a mediation model specification, estimators fs, and original dataset exper, this will re-estimate the mediation model on resampled versions of exper and apply each estimator in fs to construct bootstrap distributions associated wtih those estimators.
Arguments
- model
An object of class multimedia with specified mediation and outcome models that we want to re-estimate across B bootstrap samples.
- exper
An object of class multimedia_data containing the mediation and outcome data from which the direct effects are to be estimated.
- fs
The estimators whose bootstrap samples we are interested in. These are assumed to be a vector of functions (for example, direct_effect or indirect_effect), and they will each be applied to each bootstrap resample.
- B
The number of bootstrap samples. Defaults to 1000.
- progress
A logical indicating whether to show a progress bar.
Value
stats A list of length B containing the results of the fs applied on each of the B bootstrap resamples.
Examples
# example with null data. We set B to 5 just to execute quickly -- it's not
# actually a practical choice of B
exper <- demo_joy() |>
mediation_data("PHQ", "treatment", starts_with("ASV"))
multimedia(exper) |>
bootstrap(exper, B = 5)
#> Bootstrapping direct_effect
#> $direct_effect
#> bootstrap outcome indirect_setting contrast direct_effect
#> 1 1 PHQ Control Control - Treatment -0.2157883
#> 2 1 PHQ Treatment Control - Treatment -0.2157883
#> 3 2 PHQ Control Control - Treatment -0.4556939
#> 4 2 PHQ Treatment Control - Treatment -0.4556939
#> 5 3 PHQ Control Control - Treatment -0.4300947
#> 6 3 PHQ Treatment Control - Treatment -0.4300947
#> 7 4 PHQ Control Control - Treatment -0.9455150
#> 8 4 PHQ Treatment Control - Treatment -0.9455150
#> 9 5 PHQ Control Control - Treatment -0.4629558
#> 10 5 PHQ Treatment Control - Treatment -0.4629558
#>
# example with another dataset
exper <- demo_spline(n_samples = 100, tau = c(2, 1)) |>
mediation_data(starts_with("outcome"), "treatment", "mediator")
samples <- multimedia(exper, rf_model(num.trees = 1e3)) |>
bootstrap(exper, B = 5)
#> Bootstrapping direct_effect
ggplot2::ggplot(samples$direct_effect) +
ggplot2::geom_histogram(
ggplot2::aes(direct_effect, fill = indirect_setting),
bins = 15
) +
ggplot2::facet_wrap(~outcome, scales = "free")
