Description Usage Arguments Details Value Author(s) References Examples

Provides the ability to perform marginal mediation. Marginal mediation is particularly useful for situations where the mediator or outcome is categorical, a count, or some other non-normally distributed variable. The results provide the average marginal effects of the models, providing simple interpretation of the indirect effects.

1 |

`...` |
the glm model objects; the first is the model with the outcome while the others are the mediated effects ("a" paths) |

`ind_effects` |
a vector of the desired indirect effects. Has the form |

`ci_type` |
a string indicating the type of bootstrap method to use (currently "perc" and "basic" are available; "perc" is recommended). Further development will allow the Bias-Corrected bootstrap soon. |

`boot` |
the number of bootstrapped samples; default is 500 |

`ci` |
the confidence interval; the default is .95 which is the 95% confidence interval. |

Using the average marginal effects as discussed by Tamas Bartus (2005), the coefficients are transformed into probabilities (for binary outcomes) or remain in their original units (continuous outcomes).

A list of class `mma`

containing:

`ind_effects` |
the indirect effects reported in the average marginal effect |

`dir_effects` |
the direct effects reported in the average marginal effect |

`ci_level` |
the confidence level |

`data` |
the original data frame |

`reported_ind` |
the indirect effects the user requested (in the |

`boot` |
the number of bootstrap samples |

`model` |
the formulas of the individual sub-models |

`call` |
the original function call |

Tyson S. Barrett

Bartus, T. (2005). Estimation of marginal effects using margeff. The Stata Journal, 5(3), 309–329.

MacKinnon, D. (2008). Introduction to Statistical Mediation Analysis. Taylor \& Francis, LLC.

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## A minimal example:
library(furniture)
data(nhanes_2010)
bcpath = glm(marijuana ~ home_meals + gender + age + asthma,
data = nhanes_2010,
family = "binomial")
apath = glm(home_meals ~ gender + age + asthma,
data = nhanes_2010,
family = "gaussian")
(fit = mma(bcpath, apath,
ind_effects = c("genderFemale-home_meals",
"age-home_meals",
"asthmaNo-home_meals"),
boot = 10))
``` |

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