hi all,
I'll begin with my two question and all the related information (description of the research and the data and full output) will follow.
1. When i execute model1 (glmm with random intercept only for subjects): predictor (suppBin) and outcome (DtlsBinUp) and pre-intervention variables, it results with significance . when I carry out model 2: add the mediator (rlctDown) too as a predictor, the association shown in the model1 isn't significant anymore (suppBin-DtlsBinup), and for the mediator and outcome it is (rlctDown-dtlsBinup), with higher coefficient. that should imply for full mediation, meaning there isn't direct effect between the predictor and the outcome, only indirect. but when i the test mediation model (monte carlo method), I gel significant effect for total effect, direct effect and the indirect effect. how can it be that the monte carlo contradicts what shown when substracting model1 from model2? what am i missing?
2.i am having trouble in interpreting the values of the effects estimations in the monte carlo test. I understood the coefficients for the glmm
as log odds that after transforming using exponential function can be understood as odds and may also be expressed as probabilities. but
the estimates in the monte carlo output are much lower than those in the glmm output. so how should they be understood.
following are description and output,
thank you
uri.
********** predictor - outcome
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: dtlsBinUp ~ suppBin * qu + ageS + gender + (1 | PD)
Data: hypoTest
Control: glmerControl(tolPwrss = 0.001)
AIC BIC logLik deviance df.resid
15351.9 15406.1 -7669.0 15337.9 17111
Scaled residuals:
Min 1Q Median 3Q Max
-0.6655 -0.5281 -0.5140 -0.1889 5.4472
Random effects:
Groups Name Variance Std.Dev.
PD (Intercept) 0 0
Number of obs: 17118, groups: PD, 200
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.20574 0.14668 -21.856 < 2e-16 ***
suppBin 0.57468 0.15930 3.607 0.000309 ***
qu 2.02646 0.10902 18.588 < 2e-16 ***
ageS -0.09564 0.09923 -0.964 0.335151
gender -0.05598 0.04141 -1.352 0.176458
suppBin:qu -0.15165 0.17283 -0.877 0.380250
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) suppBn qu ageS gender
suppBin -0.495
qu -0.718 0.655
ageS -0.673 0.010 0.002
gender -0.179 0.008 0.034 0.065
suppBin:qu 0.456 -0.922 -0.631 -0.004 -0.028
********** predictor, mediator - outcome
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: dtlsBinUp ~ suppBin * qu + rlctDown + ageS + gender + (1 | PD)
Data: hypoTest
Control: glmerControl(tolPwrss = 0.001)
AIC BIC logLik deviance df.resid
14114.1 14176.0 -7049.0 14098.1 17110
Scaled residuals:
Min 1Q Median 3Q Max
-1.5239 -0.4638 -0.4552 -0.1487 6.8990
Random effects:
Groups Name Variance Std.Dev.
PD (Intercept) 0 0
Number of obs: 17118, groups: PD, 200
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.69635 0.15247 -24.24 <2e-16 ***
suppBin 0.14896 0.16475 0.90 0.366
qu 2.26040 0.11289 20.02 <2e-16 ***
rlctDown 2.06709 0.05947 34.76 <2e-16 ***
ageS -0.10680 0.10432 -1.02 0.306
gender -0.02293 0.04360 -0.53 0.599
suppBin:qu 0.13720 0.17963 0.76 0.445
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) suppBn qu rlctDw ageS gender
suppBin -0.462
qu -0.708 0.629
rlctDown -0.159 -0.088 0.143
ageS -0.665 0.000 -0.018 -0.005
gender -0.184 0.008 0.035 0.024 0.066
suppBin:qu 0.426 -0.916 -0.607 0.062 0.005 -0.029
********** predictor, mediator - outcome (function "mediate" from packege "mediation"
** script (syntax):
med.out.8.1.2.1 <- mediate(model3.1, model8.1.2.med, treat = "suppBin", mediator = "rlctDown",
sims = 1000)
Causal Mediation Analysis
Quasi-Bayesian Confidence Intervals
Mediator Groups: PD
Outcome Groups: PD
Output Based on Overall Averages Across Groups
Estimate 95% CI Lower 95% CI Upper p-value
ACME (control) 0.0401 0.0321 0.0481 0
ACME (treated) 0.0420 0.0338 0.0506 0
ADE (control) 0.0376 0.0178 0.0575 0
ADE (treated) 0.0395 0.0189 0.0595 0
Total Effect 0.0796 0.0580 0.1013 0
Prop. Mediated (control) 0.5015 0.3890 0.6852 0
Prop. Mediated (treated) 0.5276 0.4127 0.7081 0
ACME (average) 0.0410 0.0329 0.0492 0
ADE (average) 0.0385 0.0183 0.0584 0
Prop. Mediated (average) 0.5145 0.3999 0.6961 0
Sample Size Used: 17118
Simulations: 1000
I'll begin with my two question and all the related information (description of the research and the data and full output) will follow.
1. When i execute model1 (glmm with random intercept only for subjects): predictor (suppBin) and outcome (DtlsBinUp) and pre-intervention variables, it results with significance . when I carry out model 2: add the mediator (rlctDown) too as a predictor, the association shown in the model1 isn't significant anymore (suppBin-DtlsBinup), and for the mediator and outcome it is (rlctDown-dtlsBinup), with higher coefficient. that should imply for full mediation, meaning there isn't direct effect between the predictor and the outcome, only indirect. but when i the test mediation model (monte carlo method), I gel significant effect for total effect, direct effect and the indirect effect. how can it be that the monte carlo contradicts what shown when substracting model1 from model2? what am i missing?
2.i am having trouble in interpreting the values of the effects estimations in the monte carlo test. I understood the coefficients for the glmm
as log odds that after transforming using exponential function can be understood as odds and may also be expressed as probabilities. but
the estimates in the monte carlo output are much lower than those in the glmm output. so how should they be understood.
following are description and output,
thank you
uri.
********** predictor - outcome
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: dtlsBinUp ~ suppBin * qu + ageS + gender + (1 | PD)
Data: hypoTest
Control: glmerControl(tolPwrss = 0.001)
AIC BIC logLik deviance df.resid
15351.9 15406.1 -7669.0 15337.9 17111
Scaled residuals:
Min 1Q Median 3Q Max
-0.6655 -0.5281 -0.5140 -0.1889 5.4472
Random effects:
Groups Name Variance Std.Dev.
PD (Intercept) 0 0
Number of obs: 17118, groups: PD, 200
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.20574 0.14668 -21.856 < 2e-16 ***
suppBin 0.57468 0.15930 3.607 0.000309 ***
qu 2.02646 0.10902 18.588 < 2e-16 ***
ageS -0.09564 0.09923 -0.964 0.335151
gender -0.05598 0.04141 -1.352 0.176458
suppBin:qu -0.15165 0.17283 -0.877 0.380250
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) suppBn qu ageS gender
suppBin -0.495
qu -0.718 0.655
ageS -0.673 0.010 0.002
gender -0.179 0.008 0.034 0.065
suppBin:qu 0.456 -0.922 -0.631 -0.004 -0.028
********** predictor, mediator - outcome
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: dtlsBinUp ~ suppBin * qu + rlctDown + ageS + gender + (1 | PD)
Data: hypoTest
Control: glmerControl(tolPwrss = 0.001)
AIC BIC logLik deviance df.resid
14114.1 14176.0 -7049.0 14098.1 17110
Scaled residuals:
Min 1Q Median 3Q Max
-1.5239 -0.4638 -0.4552 -0.1487 6.8990
Random effects:
Groups Name Variance Std.Dev.
PD (Intercept) 0 0
Number of obs: 17118, groups: PD, 200
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.69635 0.15247 -24.24 <2e-16 ***
suppBin 0.14896 0.16475 0.90 0.366
qu 2.26040 0.11289 20.02 <2e-16 ***
rlctDown 2.06709 0.05947 34.76 <2e-16 ***
ageS -0.10680 0.10432 -1.02 0.306
gender -0.02293 0.04360 -0.53 0.599
suppBin:qu 0.13720 0.17963 0.76 0.445
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) suppBn qu rlctDw ageS gender
suppBin -0.462
qu -0.708 0.629
rlctDown -0.159 -0.088 0.143
ageS -0.665 0.000 -0.018 -0.005
gender -0.184 0.008 0.035 0.024 0.066
suppBin:qu 0.426 -0.916 -0.607 0.062 0.005 -0.029
********** predictor, mediator - outcome (function "mediate" from packege "mediation"
** script (syntax):
med.out.8.1.2.1 <- mediate(model3.1, model8.1.2.med, treat = "suppBin", mediator = "rlctDown",
sims = 1000)
Causal Mediation Analysis
Quasi-Bayesian Confidence Intervals
Mediator Groups: PD
Outcome Groups: PD
Output Based on Overall Averages Across Groups
Estimate 95% CI Lower 95% CI Upper p-value
ACME (control) 0.0401 0.0321 0.0481 0
ACME (treated) 0.0420 0.0338 0.0506 0
ADE (control) 0.0376 0.0178 0.0575 0
ADE (treated) 0.0395 0.0189 0.0595 0
Total Effect 0.0796 0.0580 0.1013 0
Prop. Mediated (control) 0.5015 0.3890 0.6852 0
Prop. Mediated (treated) 0.5276 0.4127 0.7081 0
ACME (average) 0.0410 0.0329 0.0492 0
ADE (average) 0.0385 0.0183 0.0584 0
Prop. Mediated (average) 0.5145 0.3999 0.6961 0
Sample Size Used: 17118
Simulations: 1000