[Mediation-information] Prop. Mediated vs. ACME (average).
Daniel Newman
dan.newman86 at gmail.com
Wed Jun 10 04:34:10 CEST 2015
Dear mediation gourp,
Firstly, thank you to Profs. Tingley, Yamamoto, Hirose, Keele, Imai, et
al. for building this R package. I have just started using it.
I am a little confused about the difference between "Prop. Mediated
(average)" and "ACME (average)". Could anybody help explain this to me?
For example, in the mediate summary below, the 95% CIs show that my "Prop.
Mediated (average)" is significantly greater than zero, but the ACME
(average) is not significantly greater than zero.
Causal Mediation Analysis
Quasi-Bayesian Confidence Intervals
Mediator Groups: AllConds
Outcome Groups: AllConds
Output Based on Overall Averages Across Groups
Estimate 95% CI Lower 95% CI Upper p-value
ACME (control) -0.0684 -0.1537 0.0103 0.1
ACME (treated) -0.2053 -0.4612 0.0308 0.1
ADE (control) -3.0298 -6.8686 0.4929 0.1
ADE (treated) -3.1667 -7.1734 0.5134 0.1
Total Effect -3.2351 -7.3259 0.5237 0.1
Prop. Mediated (control) 0.0213 0.0139 0.0291 0.0
Prop. Mediated (treated) 0.0640 0.0417 0.0873 0.0
*ACME (average) * -0.1369 -0.3075 0.0205 0.1
ADE (average) -3.0983 -7.0210 0.5032 0.1
*Prop. Mediated (average)* 0.0427 0.0278 0.0582 0.0
This output above was generated from the code below - a mediation model to
test whether the continuous mediator ("PrePupilDiameter ") mediates the
relationship between the categorical treatment ("Light") and the continuous
dependent variable ("RT") :
med.fit<-lmer(PrePupilDiameter ~ Light + (1 | AllConds), data =
data_LeftTargets, na.action = na.exclude)
out.fit<-lmer(RT ~ PrePupilDiameter + Light + PrePupilDiameter * Light + (1
| AllConds), data = data_LeftTargets, na.action = na.exclude)
med.out <- mediate(med.fit, out.fit, treat = "Light", mediator =
"PrePupilDiameter", control.value = 1, treat.value = 3, sims = 100,
outcome="RT")
summary(med.out)
Any help explaining to me the difference between "Prop. Mediated (average)"
and "ACME (average)" would be much appreciated
Cheers
Dan
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