[R-sig-ME] subjects assigned to treatment groups, multiple repeated measures over time
P Greenwood
pgreenw1 at gmu.edu
Fri Oct 13 17:32:39 CEST 2017
Thank you so much. I did apply for membership, but have not had a response.
To answer your questions:
1. I have currently an n of 26, with 12 subjects in one group, 14 in the other. So 5 drives x 10 trials x 12 (group 1) or 14 (group 2) subjects
2. There are 2 treatments.
3. I am pasting in the output below.
4. I don’t currently have a main effect of Condition, p = 0.059. If I did, would I be justified in running a separate model on each Condition? (I want to plan for future studies of this sort)
Thanks for the suggestions re: “build your own” tools. Let me know if you have other thoughts after seeing the output.
Pam
sumModelInteraction21 <- lmer(RT ~ 1 + Condition + PzAlpha + Drive + Condition:PzAlpha + (1 | Subject) + (1 | Trial), data = INFAST_Behavioral)
> summary(sumModelInteraction21)
Linear mixed model fit by REML
t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']
Formula: RT ~ 1 + Condition + PzAlpha + Drive + Condition:PzAlpha + (1 | Subject) + (1 | Trial)
Data: INFAST_Behavioral
REML criterion at convergence: 2619.9
Scaled residuals:
Min 1Q Median 3Q Max
-3.2633 -0.5041 -0.1428 0.2929 7.9158
Random effects:
Groups Name Variance Std.Dev.
Subject (Intercept) 0.572680 0.75676
Trial (Intercept) 0.004865 0.06975
Residual 0.442561 0.66525
Number of obs: 1231, groups: Subject, 26; Trial, 10
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.469e-01 2.249e-01 2.600e+01 -1.543 0.1350
ConditionMachineLanguage 5.214e-01 3.002e-01 2.390e+01 1.737 0.0952 .
PzAlpha 1.291e-02 2.970e-02 1.198e+03 0.435 0.6638
Drive 3.098e-02 1.331e-02 1.193e+03 2.327 0.0201 *
ConditionMachineLanguage:PzAlpha -2.073e-04 4.053e-02 1.198e+03 -0.005 0.9959
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CndtML PzAlph Drive
CndtnMchnLn -0.719
PzAlpha -0.009 0.011
Drive -0.177 0.001 -0.034
CndtnMcL:PA 0.007 -0.002 -0.732 0.020
> sumModelInteraction20 <- lmer(RT ~ 1 + Condition + PzAlpha + Drive + Condition*PzAlpha*PzDelta*Drive + (1 | Subject) + (1 | Trial), data = INFAST_Behavioral)
> summary(sumModelInteraction20)
Linear mixed model fit by REML
t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']
Formula: RT ~ 1 + Condition + PzAlpha + Drive + Condition * PzAlpha * PzDelta * Drive + (1 | Subject) + (1 | Trial)
Data: INFAST_Behavioral
REML criterion at convergence: 2676.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.3382 -0.4999 -0.1433 0.3075 7.8310
Random effects:
Groups Name Variance Std.Dev.
Subject (Intercept) 0.573459 0.75727
Trial (Intercept) 0.004913 0.07009
Residual 0.444402 0.66664
Number of obs: 1231, groups: Subject, 26; Trial, 10
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.966e-01 2.292e-01 2.790e+01 -1.730 0.0946 .
ConditionMachineLanguage 6.269e-01 3.111e-01 2.750e+01 2.015 0.0538 .
PzAlpha 4.245e-02 7.310e-02 1.188e+03 0.581 0.5616
Drive 4.701e-02 1.975e-02 1.182e+03 2.381 0.0174 *
PzDelta 4.598e-02 6.787e-02 1.187e+03 0.677 0.4983
ConditionMachineLanguage:PzAlpha -3.520e-02 9.433e-02 1.188e+03 -0.373 0.7091
ConditionMachineLanguage:PzDelta -4.527e-02 9.377e-02 1.187e+03 -0.483 0.6294
PzAlpha:PzDelta -3.334e-02 7.168e-02 1.187e+03 -0.465 0.6420
ConditionMachineLanguage:Drive -3.403e-02 2.715e-02 1.183e+03 -1.253 0.2104
PzAlpha:Drive -9.227e-03 2.172e-02 1.190e+03 -0.425 0.6710
Drive:PzDelta -2.117e-02 2.103e-02 1.187e+03 -1.007 0.3143
ConditionMachineLanguage:PzAlpha:PzDelta -6.171e-02 9.652e-02 1.188e+03 -0.639 0.5227
ConditionMachineLanguage:PzAlpha:Drive 1.088e-02 2.856e-02 1.189e+03 0.381 0.7032
ConditionMachineLanguage:Drive:PzDelta 2.344e-02 2.891e-02 1.187e+03 0.811 0.4176
PzAlpha:Drive:PzDelta 1.553e-02 2.183e-02 1.189e+03 0.711 0.4770
ConditionMachineLanguage:PzAlpha:Drive:PzDelta 1.753e-02 3.041e-02 1.188e+03 0.576 0.5645
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P.M. Greenwood, Ph.D.
Associate Professor of Psychology
Editorial Board, NeuroImage
David King Hall 2052
George Mason University
MSN 3F5, 4400 University Drive
Fairfax, VA 22030-4444
Ph: 703 993-4268
fax: 703 993-1359
email: Pgreenw1 at gmu.edu
http://psychology.gmu.edu/people/pgreenw1
> On Oct 13, 2017, at 9:43 AM, Ben Bolker <bbolker at gmail.com> wrote:
>
> On Thu, Oct 12, 2017 at 10:03 AM, P Greenwood <pgreenw1 at gmu.edu> wrote:
>> Hello
>>
>> I am a new user of lme4 and cannot find a clear answer to how to handle subjects randomly assigned to condition (treatment) groups. The subject to subject variability is likely to differ between groups on my measures in a way that varies over time. Time is broken into to 5 “drives” which occur in order (drive 1 is first, drive 2 is second, etc). Each drive has 10 trials on which various measures are obtained - heart rate, alpha band power at Pz, eye gaze, etc..
>>
>
> How much data do you have? Is the total 5 drives x 10 trials x ??
> treatments ?? subjects? How many treatments? In principle, variation
> in among-subject variance across groups over time is a sensible thing
> to want to model, but it's not easy in any of the canned frameworks I
> know of within R. Varying among-subject variation can roughly done
> via the example in ?dummy:
>
> lmer(distance ~ age + (age|Subject) +
> (0+dummy(Sex, "Female")|Subject), data = Orthodont)
>
> which effectively allows different among-subject variation for males
> and females (the big caveat here is that it can only *add* variance
> for the dummy group, so variance(male) must be < variance(female) for
> this to work ...
>
> Otherwise, you will need to use one of the "build-your-own" tools
> (Stan, rethinking, TMB, JAGS, ...)
>
>> Initially, I want to predict reaction time (RT) as a function of condition, alpha band power (PzAlpha), and drive.
>>
>> This model runs, but I’m not sure whether I am handling subjects within group correctly.
>> I plan to use Anova to compare a null model with a simpler model, once I understand how to handle subjects within groups.
>>
>> Here is my model and output as a screen shot. Thanks so very much...
>
> Screenshots get stripped from mailing list posts. Can you
> cut-and-paste as text?
>
>>
>> sumModelInteraction20 <- lmer(RT ~ 1 + Condition + PzAlpha + Drive + Condition*PzAlpha*Drive +
> (1 | Subject) + (1 | Trial), data = INFAST_Behavioral)
>
> Crudely speaking, this should allow for among-subject variation in
> the response variable (although not for random slopes
> such as varying effect of pzalpha or drive among subjects).
> Condition*PzAlpha*Drive includes all of the main effects as
> well as all two-way interactions and the three-way interactions, so
> the intercept and main effect terms in the formula are redundant (but
> harmless).
>
>>
>>
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