View source: R/Marginal_Mediation.R
mma | R Documentation |
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.
mma(..., ind_effects, ci_type = "perc", boot = 500, ci = 0.95)
... |
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.
## 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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