In TysonStanley/MarginalMediation: Marginal Mediation

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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)

devtools::load_all()

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)

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 ([e-mail]).

TysonStanley/MarginalMediation documentation built on April 17, 2022, 3:23 a.m.