Indirect Effects via Single Mediation Paths

Usage

indirect_pathwise(model, exper = NULL, t1 = 1, t2 = 2, progress = TRUE)

Arguments

model: An object of class multimedia containing the estimated mediation and outcome models whose mediation and outcome predictions we want to compare.
exper: An object of class multimedia_data containing the mediation and outcome data from which the direct effects are to be estimated.
t1: The reference level of the treatment to be used when computing the (pathwise) indirect effect.
t2: The alternative level of the treatment to be used when computing the (pathwise) indirect effect.
progress: A logical indicating whether to show a progress bar.

Value

A data.frame summarizing the pathwise (per-mediator) indirect effects associated with different settings of the direct effect.

Examples

# example with null data
exper <- demo_joy() |>
    mediation_data("PHQ", "treatment", starts_with("ASV"))
fit <- multimedia(exper) |>
    estimate(exper)
indirect_pathwise(fit)
#> Indirect effects for direct setting 1
#> Indirect effects for direct setting 2
#>    outcome mediator direct_setting            contrast indirect_effect
#> 1      PHQ     ASV1        Control Control - Treatment   -4.833402e-03
#> 2      PHQ     ASV2        Control Control - Treatment    4.686034e-02
#> 3      PHQ     ASV3        Control Control - Treatment    4.236937e-03
#> 4      PHQ     ASV4        Control Control - Treatment   -9.571373e-05
#> 5      PHQ     ASV5        Control Control - Treatment    1.221456e-03
#> 6      PHQ     ASV1      Treatment Control - Treatment   -4.833402e-03
#> 7      PHQ     ASV2      Treatment Control - Treatment    4.686034e-02
#> 8      PHQ     ASV3      Treatment Control - Treatment    4.236937e-03
#> 9      PHQ     ASV4      Treatment Control - Treatment   -9.571373e-05
#> 10     PHQ     ASV5      Treatment Control - Treatment    1.221456e-03

# example with another dataset
exper <- demo_spline(tau = c(2, 1)) |>
    mediation_data(starts_with("outcome"), "treatment", "mediator")
fit <- multimedia(exper) |>
    estimate(exper)
indirect_pathwise(fit)
#> Indirect effects for direct setting 1
#> Indirect effects for direct setting 2
#>     outcome mediator direct_setting contrast indirect_effect
#> 1 outcome_1 mediator              0    0 - 1     -0.02314333
#> 2 outcome_2 mediator              0    0 - 1      0.20036985
#> 3 outcome_1 mediator              1    0 - 1     -0.02314333
#> 4 outcome_2 mediator              1    0 - 1      0.20036985