# MarginalMediation: 0.7.0

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)
#> ── furniture 1.9.0 ─────────────────────────────────────────────────────────────── learn more at tysonbarrett.com ──
#> ✔ furniture attached
#> ✔ No potential conflicts found
data(nhanes_2010)
```

```
## The MarginalMediation package
library(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:

- the individual regression results,
- the
`a`

paths, - the
`b`

paths, - the indirect effect with the confidence interval, and
- 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).