" class="reference-link">MarginalMediation: v 0.7.2

The MarginalMediation package provides the ability to perform
marginal mediation analysis. It provides a useful statistical
framework from which to interpret the coefficients in a mediation
analysis, especially when the mediator(s) and/or outcome is binary or a
count (other types of outcomes will be added).

You can install it via:

install.packages("MarginalMediation")

or

install.packages("remotes")
remotes::install_github("tysonstanley/MarginalMediation")

The main function is mma():

mma(...,
    ind_effects = c("apath-bpath"))

where ... consists of 2 or more model objects. The first is the b
and c' path model, while the others are the a path models.

The ind_effects is a vector of requested mediated paths. These
estimates are in terms of the average marginal effects using the a x b
method of estimating indirect paths. Any number of these can be
included, although it is limited to the number of variables available in
the models.

A Quick Example

Below is an example, where the theoretical backing of such a model is
not very stable, but it is useful to show how to use the function and
the output.

## Data for the example
library(furniture)
data(nhanes_2010)

## The MarginalMediation package
library(MarginalMediation)

#> ℹ Loading MarginalMediation
#> ── MarginalMediation 0.7.2 ─────────────────── learn more at tysonbarrett.com ──
#> ✓ MarginalMediation attached
#> ✓ No potential conflicts found

pathbc <- glm(marijuana ~ home_meals + gender + age + asthma, 
              data = nhanes_2010, 
              family = "binomial")
patha <- glm(home_meals ~ gender + age + asthma,
             data = nhanes_2010, 
             family = "gaussian")
mma(pathbc, patha,
    ind_effects = c("genderFemale-home_meals",
                    "age-home_meals",
                    "asthmaNo-home_meals"),
    boot = 500)
#> 
#> calculating a paths... b and c paths... Done.                                                                                 
#> ┌───────────────────────────────┐
#> │  Marginal Mediation Analysis  │
#> └───────────────────────────────┘
#> A marginal mediation model with:
#>    1 mediators
#>    3 indirect effects
#>    3 direct effects
#>    500 bootstrapped samples
#>    95% confidence interval
#>    n = 1417 
#> 
#> Formulas:
#>    ◌ marijuana ~ home_meals + gender + age + asthma
#>    ◌ home_meals ~ gender + age + asthma 
#> 
#> Regression Models: 
#> 
#>      marijuana ~ 
#>                           Est      SE   Est/SE P-Value
#>         (Intercept)  -0.39400 0.38028 -1.03608 0.30017
#>         home_meals   -0.04062 0.01363 -2.98051 0.00288
#>         genderFemale  0.43161 0.11723  3.68169 0.00023
#>         age           0.00276 0.01470  0.18754 0.85123
#>         asthmaNo     -0.00717 0.15004 -0.04778 0.96189
#> 
#>      home_meals ~ 
#>                           Est      SE   Est/SE P-Value
#>         (Intercept)   6.56883 0.76462  8.59100 0.00000
#>         genderFemale -1.34831 0.23910 -5.63913 0.00000
#>         age          -0.05689 0.03017 -1.88565 0.05955
#>         asthmaNo     -0.00428 0.31293 -0.01368 0.98909
#> 
#> Unstandardized Mediated Effects: 
#> 
#>    Indirect Effects: 
#> 
#>      marijuana ~ 
#>                                    Indirect    Lower   Upper
#>         genderFemale => home_meals  0.01312  0.00357 0.02435
#>         age => home_meals           0.00055  0.00000 0.00143
#>         asthmaNo => home_meals      0.00004 -0.00652 0.00596
#> 
#>    Direct Effects: 
#> 
#>      marijuana ~ 
#>                        Direct    Lower   Upper
#>         genderFemale  0.10430  0.04950 0.16045
#>         age           0.00066 -0.00652 0.00793
#>         asthmaNo     -0.00172 -0.07896 0.06809

The print method provides:

1. the individual regression results,
2. the a paths,
3. the b paths,
4. the indirect effect with the confidence interval, and
5. the direct effect with the confidence interval.

The regressions are in their original (non-AME) units while the indirect
and direct effects are in the AME units—the units of the outcome—in this
case, risk of using marijuana.

Conclusions

Let me know if you find any bugs or want to discuss the method
(t.barrett@aggiemail.usu.edu).