ACME: 獨變項對依變項的間接作用
ADE: 獨變項對依變項的直接作用
Total effect: ACME+ADE 總效應
Prop: 中介效應的占比,由間接效應除以直接效應而得
study2_1<-split(study2,study2$RECGROUP)
fit6_1<-lm(score~Decentering,data=study2_1$'1')
fit6_2<-lm(SS~Decentering+score,data=study2_1$'1')
fit6<-mediate(fit6_1,fit6_2,treat="Decentering",mediator="score")
summary(fit6)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.0124 -0.0214 0.06 0.52
## ADE 0.3145 0.1416 0.49 <2e-16 ***
## Total Effect 0.3269 0.1504 0.50 <2e-16 ***
## Prop. Mediated 0.0284 -0.0807 0.20 0.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 59
##
##
## Simulations: 1000
plot(fit6)
fit7_1<-lm(score~Decentering,data=study2_1$'2')
fit7_2<-lm(SS~Decentering+score,data=study2_1$'2')
fit7<-mediate(fit7_1,fit7_2,treat="Decentering",mediator="score")
summary(fit7)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.02264 -0.00982 0.07 0.194
## ADE 0.17686 0.02558 0.33 0.026 *
## Total Effect 0.19950 0.04641 0.36 0.014 *
## Prop. Mediated 0.09935 -0.07829 0.48 0.196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 70
##
##
## Simulations: 1000
plot(fit7)
fit8_1<-lm(score~Decentering,data=study2_1$'9')
fit8_2<-lm(SS~Decentering+score,data=study2_1$'9')
fit8<-mediate(fit8_1,fit8_2,treat="Decentering",mediator="score")
summary(fit8)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.000885 -0.094037 0.10 1.00
## ADE 0.243704 0.014754 0.48 0.03 *
## Total Effect 0.242819 0.024060 0.47 0.03 *
## Prop. Mediated 0.000364 -0.904747 0.55 0.99
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 17
##
##
## Simulations: 1000
plot(fit8)
fit9_1<-lm(Decentering~SS,data=study2_1$"1")
fit9_2<-lm(score~SS+Decentering,data=study2_1$"1")
fit9<-mediate(fit9_1,fit9_2,treat="SS",mediator="Decentering")
summary(fit9)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.265 -1.253 0.57 0.56
## ADE -0.935 -2.867 0.92 0.35
## Total Effect -1.200 -2.911 0.59 0.19
## Prop. Mediated 0.164 -1.723 2.70 0.65
##
## Sample Size Used: 59
##
##
## Simulations: 1000
plot(fit9)
fit10_1<-lm(Decentering~SS,data=study2_1$"2")
fit10_2<-lm(score~SS+Decentering,data=study2_1$"2")
fit10<-mediate(fit10_1,fit10_2,treat="SS",mediator="Decentering")
summary(fit10)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.2751 -1.2462 0.47 0.478
## ADE -2.2902 -4.8859 0.31 0.104
## Total Effect -2.5652 -5.0384 -0.14 0.036 *
## Prop. Mediated 0.0935 -0.3406 1.19 0.498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 70
##
##
## Simulations: 1000
plot(fit10)
fit11_1<-lm(Decentering~SS,data=study2_1$"9")
fit11_2<-lm(score~SS+Decentering,data=study2_1$"9")
fit11<-mediate(fit11_1,fit11_2,treat="SS",mediator="Decentering")
summary(fit11)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -1.901 -7.368 1.90 0.37
## ADE 0.146 -7.357 7.47 0.95
## Total Effect -1.755 -8.727 4.95 0.61
## Prop. Mediated 0.226 -7.897 9.50 0.76
##
## Sample Size Used: 17
##
##
## Simulations: 1000
plot(fit11)
fit12_1<-lm(Decentering~SS,data=study2_1$"1")
fit12_2<-lm(negright~SS+Decentering,data=study2_1$"1")
fit12<-mediate(fit12_1,fit12_2,treat="SS",mediator="Decentering")
summary(fit12)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.307 -0.954 0.33 0.30
## ADE -0.620 -1.940 0.66 0.35
## Total Effect -0.926 -2.074 0.28 0.13
## Prop. Mediated 0.288 -1.939 2.61 0.37
##
## Sample Size Used: 59
##
##
## Simulations: 1000
plot(fit12)
fit13_1<-lm(Decentering~SS,data=study2_1$"2")
fit13_2<-lm(negright~SS+Decentering,data=study2_1$"2")
fit13<-mediate(fit13_1,fit13_2,treat="SS",mediator="Decentering")
summary(fit13)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.0444 -0.5239 0.43 0.850
## ADE -1.5566 -3.0272 -0.01 0.046 *
## Total Effect -1.6010 -3.0630 -0.18 0.028 *
## Prop. Mediated 0.0205 -0.4272 0.67 0.854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 70
##
##
## Simulations: 1000
plot(fit13)
fit14_1<-lm(Decentering~SS,data=study2_1$"1")
fit14_2<-lm(totreg~SS+Decentering,data=study2_1$"1")
fit14<-mediate(fit14_1,fit14_2,treat="SS",mediator="Decentering")
summary(fit14)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.3037 -0.7772 0.12 0.16
## ADE -0.0716 -1.0416 0.87 0.88
## Total Effect -0.3753 -1.1877 0.46 0.39
## Prop. Mediated 0.4461 -11.3989 8.30 0.49
##
## Sample Size Used: 59
##
##
## Simulations: 1000
plot(fit14)
fit15_1<-lm(Decentering~SS,data=study2_1$"2")
fit15_2<-lm(totreg~SS+Decentering,data=study2_1$"2")
fit15<-mediate(fit15_1,fit15_2,treat="SS",mediator="Decentering")
summary(fit15)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.0426 -0.2612 0.40 0.76
## ADE 0.1303 -0.7418 1.08 0.81
## Total Effect 0.1728 -0.6490 1.08 0.72
## Prop. Mediated 0.0245 -3.0969 5.63 0.94
##
## Sample Size Used: 70
##
##
## Simulations: 1000
plot(fit15)
fit16_1<-lm(Decentering~SS,data=study2_1$"1")
fit16_2<-lm(negregscore~SS+Decentering,data=study2_1$"1")
fit16<-mediate(fit16_1,fit16_2,treat="SS",mediator="Decentering")
summary(fit16)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.340 -0.766 -0.01 0.038 *
## ADE -0.263 -0.947 0.47 0.442
## Total Effect -0.603 -1.257 0.08 0.084 .
## Prop. Mediated 0.517 -2.042 2.79 0.122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 59
##
##
## Simulations: 1000
plot(fit16)
fit17_1<-lm(negright~Decentering,data=study2_1$'1')
fit17_2<-lm(SS~Decentering+negright,data=study2_1$'1')
fit17<-mediate(fit17_1,fit17_2,treat="Decentering",mediator="negright")
summary(fit17)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.0172 -0.0202 0.07 0.41
## ADE 0.3121 0.1324 0.48 <2e-16 ***
## Total Effect 0.3293 0.1494 0.50 <2e-16 ***
## Prop. Mediated 0.0409 -0.0691 0.23 0.41
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 59
##
##
## Simulations: 1000
plot(fit17)
fit18_1<-lm(negright~Decentering,data=study2_1$'2')
fit18_2<-lm(SS~Decentering+negright,data=study2_1$'2')
fit18<-mediate(fit18_1,fit18_2,treat="Decentering",mediator="negright")
summary(fit18)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.0147 -0.0217 0.06 0.424
## ADE 0.1869 0.0471 0.33 0.010 **
## Total Effect 0.2015 0.0559 0.34 0.006 **
## Prop. Mediated 0.0590 -0.1628 0.37 0.426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 70
##
##
## Simulations: 1000
plot(fit18)
fit19_1<-lm(totreg~Decentering,data=study2_1$'1')
fit19_2<-lm(SS~Decentering+totreg,data=study2_1$'1')
fit19<-mediate(fit19_1,fit19_2,treat="Decentering",mediator="totreg")
summary(fit19)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.00202 -0.04619 0.05 0.95
## ADE 0.32170 0.15189 0.50 <2e-16 ***
## Total Effect 0.32371 0.14755 0.50 <2e-16 ***
## Prop. Mediated 0.00212 -0.19544 0.17 0.95
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 59
##
##
## Simulations: 1000
plot(fit19)
fit20_1<-lm(totreg~Decentering,data=study2_1$'2')
fit20_2<-lm(SS~Decentering+totreg,data=study2_1$'2')
fit20<-mediate(fit20_1,fit20_2,treat="Decentering",mediator="totreg")
summary(fit20)
##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.00134 -0.01990 0.02 0.886
## ADE 0.19661 0.04776 0.35 0.020 *
## Total Effect 0.19795 0.04838 0.35 0.022 *
## Prop. Mediated 0.00236 -0.12858 0.15 0.880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 70
##
##
## Simulations: 1000
plot(fit20)