# mma

0th

Percentile

##### Marginal Mediation

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.

##### Usage
mma(..., ind_effects, ci_type = "perc", boot = 500, ci = 0.95)
##### Arguments
...

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 "var1-var2".

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.

##### Details

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).

##### Value

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

##### References

Bartus, T. (2005). Estimation of marginal effects using margeff. The Stata Journal, 5(3), 309<U+2013>329.

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

• mma
##### Examples
# NOT RUN {
## 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))

# }

Documentation reproduced from package MarginalMediation, version 0.7.0, License: GPL-2

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