This vignettes demonstrates the mediation()-function.
Before we start, we fit some models, including a mediation-object from
the mediation-package and a structural equation modelling
approach with the lavaan-package, both of which we use for
comparison with brms and rstanarm.
Mediation Analysis in brms and rstanarm
library(bayestestR)
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 brms
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 <- 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
)mediation() is a summary function, especially for
mediation analysis, i.e. for multivariate response models with casual
mediation effects.
In the models m2 and m3, treat
is the treatment effect and job_seek is the mediator
effect. For the brms model (m2), f1
describes the mediator model and f2 describes the outcome
model. This is similar for the rstanarm model.
mediation() returns a data frame with information on the
direct effect (median value of posterior samples from treatment
of the outcome model), mediator effect (median value of
posterior samples from mediator of the outcome model), indirect
effect (median 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 (median
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.
The simplest call just needs the model-object.
# for brms
mediation(m2)
#> # Causal Mediation Analysis for Stan Model
#>
#> Treatment: treat
#> Mediator : job_seek
#> Response : depress2
#>
#> Effect | Estimate | 95% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE) | -0.040 | [-0.124, 0.046]
#> Indirect Effect (ACME) | -0.015 | [-0.041, 0.008]
#> Mediator Effect | -0.240 | [-0.294, -0.185]
#> Total Effect | -0.055 | [-0.145, 0.034]
#>
#> Proportion mediated: 28.14% [-181.46%, 237.75%]
# for rstanarm
mediation(m3)
#> # Causal Mediation Analysis for Stan Model
#>
#> Treatment: treat
#> Mediator : job_seek
#> Response : depress2
#>
#> Effect | Estimate | 95% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE) | -0.040 | [-0.129, 0.048]
#> Indirect Effect (ACME) | -0.018 | [-0.042, 0.006]
#> Mediator Effect | -0.241 | [-0.296, -0.187]
#> Total Effect | -0.057 | [-0.151, 0.033]
#>
#> Proportion mediated: 30.59% [-221.09%, 282.26%]Typically, mediation() finds the treatment and mediator
variables automatically. If this does not work, use the
treatment and mediator arguments to specify
the related variable names. For all values, the 89% credible intervals
are calculated by default. Use ci to calculate a different
interval.
Comparison to the mediation package
Here is a comparison with the mediation package. Note that
the summary()-output of the mediation package
shows the indirect effect first, followed by the direct effect.
summary(m1)
#>
#> Causal Mediation Analysis
#>
#> Quasi-Bayesian Confidence Intervals
#>
#> Estimate 95% CI Lower 95% CI Upper p-value
#> ACME -0.0157 -0.0387 0.01 0.19
#> ADE -0.0438 -0.1315 0.04 0.35
#> Total Effect -0.0595 -0.1530 0.02 0.21
#> Prop. Mediated 0.2137 -2.0277 2.70 0.32
#>
#> Sample Size Used: 899
#>
#>
#> Simulations: 1000
mediation(m2, ci = 0.95)
#> # Causal Mediation Analysis for Stan Model
#>
#> Treatment: treat
#> Mediator : job_seek
#> Response : depress2
#>
#> Effect | Estimate | 95% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE) | -0.040 | [-0.124, 0.046]
#> Indirect Effect (ACME) | -0.015 | [-0.041, 0.008]
#> Mediator Effect | -0.240 | [-0.294, -0.185]
#> Total Effect | -0.055 | [-0.145, 0.034]
#>
#> Proportion mediated: 28.14% [-181.46%, 237.75%]
mediation(m3, ci = 0.95)
#> # Causal Mediation Analysis for Stan Model
#>
#> Treatment: treat
#> Mediator : job_seek
#> Response : depress2
#>
#> Effect | Estimate | 95% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE) | -0.040 | [-0.129, 0.048]
#> Indirect Effect (ACME) | -0.018 | [-0.042, 0.006]
#> Mediator Effect | -0.241 | [-0.296, -0.187]
#> Total Effect | -0.057 | [-0.151, 0.033]
#>
#> Proportion mediated: 30.59% [-221.09%, 282.26%]If you want to calculate mean instead of median values from the
posterior samples, use the centrality-argument.
Furthermore, there is a print()-method, which allows to
print more digits.
m <- mediation(m2, centrality = "mean", ci = 0.95)
print(m, digits = 4)
#> # Causal Mediation Analysis for Stan Model
#>
#> Treatment: treat
#> Mediator : job_seek
#> Response : depress2
#>
#> Effect | Estimate | 95% ETI
#> ------------------------------------------------------
#> Direct Effect (ADE) | -0.0395 | [-0.1237, 0.0456]
#> Indirect Effect (ACME) | -0.0158 | [-0.0405, 0.0083]
#> Mediator Effect | -0.2401 | [-0.2944, -0.1846]
#> Total Effect | -0.0553 | [-0.1454, 0.0341]
#>
#> Proportion mediated: 28.60% [-181.01%, 238.20%]As you can see, the results are similar to what the mediation package produces for non-Bayesian models.
Comparison to SEM from the lavaan package
Finally, we also compare the results to a SEM model, using lavaan. This example should demonstrate how to “translate” the same model in different packages or modeling approached.
library(lavaan)
data(jobs)
set.seed(1234)
model <- " # direct effects
depress2 ~ c1*treat + c2*econ_hard + c3*sex + c4*age + b*job_seek
# mediation
job_seek ~ a1*treat + a2*econ_hard + a3*sex + a4*age
# indirect effects (a*b)
indirect_treat := a1*b
indirect_econ_hard := a2*b
indirect_sex := a3*b
indirect_age := a4*b
# total effects
total_treat := c1 + (a1*b)
total_econ_hard := c2 + (a2*b)
total_sex := c3 + (a3*b)
total_age := c4 + (a4*b)
"
m4 <- sem(model, data = jobs)
summary(m4)
#> lavaan 0.6-19 ended normally after 1 iteration
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 11
#>
#> Number of observations 899
#>
#> Model Test User Model:
#>
#> Test statistic 0.000
#> Degrees of freedom 0
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> depress2 ~
#> treat (c1) -0.040 0.043 -0.929 0.353
#> econ_hard (c2) 0.149 0.021 7.156 0.000
#> sex (c3) 0.107 0.041 2.604 0.009
#> age (c4) 0.001 0.002 0.332 0.740
#> job_seek (b) -0.240 0.028 -8.524 0.000
#> job_seek ~
#> treat (a1) 0.066 0.051 1.278 0.201
#> econ_hard (a2) 0.053 0.025 2.167 0.030
#> sex (a3) -0.008 0.049 -0.157 0.875
#> age (a4) 0.005 0.002 1.983 0.047
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .depress2 0.373 0.018 21.201 0.000
#> .job_seek 0.524 0.025 21.201 0.000
#>
#> Defined Parameters:
#> Estimate Std.Err z-value P(>|z|)
#> indirect_treat -0.016 0.012 -1.264 0.206
#> indirct_cn_hrd -0.013 0.006 -2.100 0.036
#> indirect_sex 0.002 0.012 0.157 0.875
#> indirect_age -0.001 0.001 -1.932 0.053
#> total_treat -0.056 0.045 -1.244 0.214
#> total_econ_hrd 0.136 0.022 6.309 0.000
#> total_sex 0.109 0.043 2.548 0.011
#> total_age -0.000 0.002 -0.223 0.824
# just to have the numbers right at hand and you don't need to scroll up
mediation(m2, ci = 0.95)
#> # Causal Mediation Analysis for Stan Model
#>
#> Treatment: treat
#> Mediator : job_seek
#> Response : depress2
#>
#> Effect | Estimate | 95% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE) | -0.040 | [-0.124, 0.046]
#> Indirect Effect (ACME) | -0.015 | [-0.041, 0.008]
#> Mediator Effect | -0.240 | [-0.294, -0.185]
#> Total Effect | -0.055 | [-0.145, 0.034]
#>
#> Proportion mediated: 28.14% [-181.46%, 237.75%]The summary output from lavaan is longer, but we can find the related numbers quite easily:
- the direct effect of treatment is
treat (c1), which is-0.040 - the indirect effect of treatment is
indirect_treat, which is-0.016 - the mediator effect of job_seek is
job_seek (b), which is-0.240 - the total effect is
total_treat, which is-0.056