R: Summary of Bayesian multivariate-response mediation-models

mediation {bayestestR}

R Documentation

Summary of Bayesian multivariate-response mediation-models

Description

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.

Usage

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",
  ...
)

Arguments

`model`	A `brmsfit` or `stanmvreg` object.
`...`	Not used.
`treatment`	Character, name of the treatment variable (or direct effect) in a (multivariate response) mediator-model. If missing, `mediation()` tries to find the treatment variable automatically, however, this may fail.
`mediator`	Character, name of the mediator variable in a (multivariate response) mediator-model. If missing, `mediation()` tries to find the treatment variable automatically, however, this may fail.
`response`	A named character vector, indicating the names of the response variables to be used for the mediation analysis. Usually can be `NULL`, in which case these variables are retrieved automatically. If not `NULL`, names should match the names of the model formulas, `names(insight::find_response(model, combine = TRUE))`. This can be useful if, for instance, the mediator variable used as predictor has a different name from the mediator variable used as response. This might occur when the mediator is transformed in one model, but used "as is" as response variable in the other model. Example: The mediator `m` is used as response variable, but the centered version `m_center` is used as mediator variable. The second response variable (for the treatment model, with the mediator as additional predictor), `y`, is not transformed. Then we could use `response` like this: `mediation(model, response = c(m = "m_center", y = "y"))`.
`centrality`	The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: `"median"`, `"mean"`, `"MAP"` (see `map_estimate()`), `"trimmed"` (which is just `mean(x, trim = threshold)`), `"mode"` or `"all"`.
`ci`	Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to `0.95` (`⁠95%⁠`).
`method`	Can be "ETI" (default), "HDI", "BCI", "SPI" or "SI".

Details

mediation() returns a data frame with information on the direct effect (mean value of posterior samples from treatment of the outcome model), mediator effect (mean value of posterior samples from 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. Use ci to calculate a different interval.

The arguments treatment and 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.

Value

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 samples (use centrality for other centrality indices).

Note

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.

References

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.

Examples



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, refresh = 0)

# Fit Bayesian mediation model in rstanarm
m3 <- suppressWarnings(stan_mvmer(
  list(
    job_seek ~ treat + econ_hard + sex + age + (1 | occp),
    depress2 ~ treat + job_seek + econ_hard + sex + age + (1 | occp)
  ),
  data = jobs,
  refresh = 0
))

summary(m1)
mediation(m2, centrality = "mean", ci = 0.95)
mediation(m3, centrality = "mean", ci = 0.95)

[Package bayestestR version 0.14.0 Index]