mediation() is a short summary for multivariate-response
mediation-models, i.e. this function computes average direct and average
causal mediation effects of multivariate response models.
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mediation(model, ...) ## S3 method for class 'brmsfit' mediation( model, treatment, mediator, response = NULL, centrality = "median", ci = 0.95, method = "ETI", ... ) ## S3 method for class 'stanmvreg' mediation( model, treatment, mediator, response = NULL, centrality = "median", ci = 0.95, method = "ETI", ... )
Character, name of the treatment variable (or direct effect)
in a (multivariate response) mediator-model. If missing,
Character, name of the mediator variable in a (multivariate
response) mediator-model. If missing,
A named character vector, indicating the names of the response
variables to be used for the mediation analysis. Usually can be
The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options:
Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to
Can be 'ETI' (default), 'HDI', 'BCI' or 'SI'.
mediation() returns a data frame with information on the
direct effect (mean value of posterior samples from
of the outcome model), mediator effect (mean value of posterior
mediator of the outcome model), indirect effect
(mean value of the multiplication of the posterior samples from
mediator of the outcome model and the posterior samples from
treatment of the mediation model) and the total effect (mean
value of sums of posterior samples used for the direct and indirect
effect). The proportion mediated is the indirect effect divided
by the total effect.
For all values, the
89% credible intervals are calculated by default.
ci to calculate a different interval.
mediator do not necessarily
need to be specified. If missing,
mediation() tries to find the
treatment and mediator variable automatically. If this does not work,
specify these variables.
The direct effect is also called average direct effect (ADE), the indirect effect is also called average causal mediation effects (ACME). See also Tingley et al. 2014 and Imai et al. 2010.
A data frame with direct, indirect, mediator and
total effect of a multivariate-response mediation-model, as well as the
proportion mediated. The effect sizes are median values of the posterior
centrality for other centrality indices).
There is an
as.data.frame() method that returns the posterior
samples of the effects, which can be used for further processing in the
different bayestestR package.
Imai, K., Keele, L. and Tingley, D. (2010) A General Approach to Causal Mediation Analysis, Psychological Methods, Vol. 15, No. 4 (December), pp. 309-334.
Tingley, D., Yamamoto, T., Hirose, K., Imai, K. and Keele, L. (2014). mediation: R package for Causal Mediation Analysis, Journal of Statistical Software, Vol. 59, No. 5, pp. 1-38.
The mediation package for a causal mediation analysis in the frequentist framework.
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## Not run: library(mediation) library(brms) library(rstanarm) # load sample data data(jobs) set.seed(123) # linear models, for mediation analysis b1 <- lm(job_seek ~ treat + econ_hard + sex + age, data = jobs) b2 <- lm(depress2 ~ treat + job_seek + econ_hard + sex + age, data = jobs) # mediation analysis, for comparison with Stan models m1 <- mediate(b1, b2, sims = 1000, treat = "treat", mediator = "job_seek") # Fit Bayesian mediation model in brms f1 <- bf(job_seek ~ treat + econ_hard + sex + age) f2 <- bf(depress2 ~ treat + job_seek + econ_hard + sex + age) m2 <- brm(f1 + f2 + set_rescor(FALSE), data = jobs, cores = 4, refresh = 0) # Fit Bayesian mediation model in rstanarm m3 <- stan_mvmer( list( job_seek ~ treat + econ_hard + sex + age + (1 | occp), depress2 ~ treat + job_seek + econ_hard + sex + age + (1 | occp) ), data = jobs, cores = 4, refresh = 0 ) summary(m1) mediation(m2, centrality = "mean", ci = .95) mediation(m3, centrality = "mean", ci = .95) ## End(Not run)
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