Provides the ability to perform "Marginal Mediation"--mediation
wherein the indirect and direct effects are in terms of the average marginal effects
(Bartus, 2005, < https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>).
The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package.
This framework allows the use of categorical mediators and outcomes with little change in interpretation
from the continuous mediators/outcomes. See
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.
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 examplelibrary(furniture)#> ── furniture 1.9.0 ─────────────────────────────────────────────────────────────── learn more at tysonbarrett.com ──#> ✔ furniture attached#> ✔ No potential conflicts founddata(nhanes_2010)
## The MarginalMediation packagelibrary(MarginalMediation)
#> Loading MarginalMediation
#> ── MarginalMediation 0.7.0 ─────────────────────────────────────────────────────── 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 0.00429 0.02562#> age => home_meals 0.00055 0.00003 0.00139#> asthmaNo => home_meals 0.00004 -0.00639 0.00672#>#> Direct Effects:#>#> marijuana ~#> Direct Lower Upper#> genderFemale 0.10430 0.04813 0.15967#> age 0.00066 -0.00603 0.00848#> asthmaNo -0.00172 -0.06947 0.07061
The print method provides:
a
paths,b
paths,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.
Let me know if you find any bugs or want to discuss the method ([email protected]).
betareg::betareg()
models.This is the initial release. The main function is mma()
, which performs the marginal mediation analysis. Other functions are mainly helper functions in interpreting and pulling information out of a mma
object.
This is beta software, so please report any issues!