[R] question: mediation results are not in line with compression of glmm consisted models
Bert Gunter
bgunter.4567 at gmail.com
Sun Apr 23 20:45:18 CEST 2017
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about statistics, not programming in R. I would suggest that you get
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-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Sun, Apr 23, 2017 at 6:53 AM, Uri Blasbalg <uriblasbalg at gmail.com> wrote:
> 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
>
> [[alternative HTML version deleted]]
>
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