From dan.newman86 at gmail.com Wed Jun 10 04:34:10 2015 From: dan.newman86 at gmail.com (Daniel Newman) Date: Wed, 10 Jun 2015 12:34:10 +1000 Subject: [Mediation-information] Prop. Mediated vs. ACME (average). Message-ID: 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From kimai at Princeton.Edu Sat Jun 13 16:46:37 2015 From: kimai at Princeton.Edu (Kosuke Imai) Date: Sat, 13 Jun 2015 10:46:37 -0400 Subject: [Mediation-information] Prop. Mediated vs. ACME (average). In-Reply-To: References: Message-ID: Prop Mediated is a ratio of two estimates, which are known to have a very high variance especially when the average treatment effect is not precisely estimated. I would focus on the point estimate of this quantity rather than its CI. The most important thing is the ACME which have a much more stable CI. Kosuke Imai Department of Politics Princeton University http://imai.princeton.edu On Tue, Jun 9, 2015 at 10:34 PM, Daniel Newman wrote: > 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 > > > _______________________________________________ > Mediation-information mailing list > Mediation-information at lists.r-forge.r-project.org > > https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/mediation-information > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jyoo32 at wisc.edu Sun Jun 28 21:41:41 2015 From: jyoo32 at wisc.edu (jyoo32 at wisc.edu) Date: Sun, 28 Jun 2015 14:41:41 -0500 Subject: [Mediation-information] An error message Message-ID: <0339A995-832A-49AD-831F-7EC0F644A645@uwnet.wisc.edu> Hi, as I ran the mediation analysis using mediate package, I got this error msg "number of observations do not match between mediator and outcome models?. Do you know how to fix this problem? Your answer would be greatly appreciated! From dtingley at gov.harvard.edu Mon Jun 29 15:40:30 2015 From: dtingley at gov.harvard.edu (dustin tingley) Date: Mon, 29 Jun 2015 13:40:30 +0000 Subject: [Mediation-information] An error message In-Reply-To: <0339A995-832A-49AD-831F-7EC0F644A645@uwnet.wisc.edu> References: <0339A995-832A-49AD-831F-7EC0F644A645@uwnet.wisc.edu> Message-ID: Hi, This means you have different sets of missing values between your mediator and outcome models. Either fill in those missing values with their actual values, or set dropobs = TRUE as described in the help file for mediate. ?mediate Our vignette published in the Journal of Statistical Software also describes an imputation approach to this issue. best, Dustin On Mon, Jun 29, 2015 at 9:37 AM wrote: > Hi, > as I ran the mediation analysis using mediate package, I got this error > msg "number of observations do not match between mediator and outcome > models?. Do you know how to fix this problem? Your answer would be greatly > appreciated! > _______________________________________________ > Mediation-information mailing list > Mediation-information at lists.r-forge.r-project.org > > https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/mediation-information > -------------- next part -------------- An HTML attachment was scrubbed... URL: