[R-sig-ME] subjects within groups and effects of group
Phillip Alday
phillip.alday at mpi.nl
Fri Jan 19 11:58:25 CET 2018
Dear Pam, (dear Thierry,)
if I'm reading the description correctly, Pam is conceiving of Trial as
being an "Item"-type factor (crossed with subject). To rephrase
Thierry's comment a bit -- if Trial corresponds to an Item (concrete
stimulus realization sampled from the population of possible stimuli for
this manipulation) that is the same across subjects, then this is a good
way to model that. If Trial doesn't correspond to an invariant set of
items, but is rather just repetitions of the same task (perhaps with
some random variation that isn't identical across subjects), then
modeling Trial as a random effect doesn't really help much. However, if
Trial is just a sequence number for the repetition, it might make sense
to instead include Trial as a continuous fixed effect in order to model
adaptation effects.
Best,
Phillip
On 19/01/18 10:44, Thierry Onkelinx wrote:
> Dear Pam,
>
> You are handling condition and subject correctly.
>
> There might be a problem with trial. Does trial indicates dependent
> replication of the study? Is there a common effect of trial X for all
> subjects? Because that is what your current model assumes. In case the
> trials are independent, then you don't need to include it in the
> model.
>
> Note that Condition + PzAlpha + PzAlpha*Condition is verbose. You can
> write it as PzAlpha*Condition.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
> AND FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx at inbo.be
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>
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>
>
> 2018-01-18 18:51 GMT+01:00 P Greenwood <pgreenw1 at gmu.edu>:
>> Hello
>>
>> I wanted some advice about handling subjects within groups and effects of group (randomly assigned). I want to predict reaction time (RT) as a function of “Condition,” alpha band power (PzAlpha), and drive. People (subjects) are randomly assigned to Condition, of which there are two. Each person has data from 5 drives, and for each drive there are 10 trials. There are 19 subjects in one group and 20 in the other.
>>
>> My question is this: Am I handling the “between subjects” factor of Condition correctly? Also, am I treating subjects within group correctly? I am pasting in some of my data. The output is below.
>>
>> Regards
>>
>> Pam Greenwood
>>
>> library(lme4)
>> library(lmerTest)
>> INFAST_Behavioral <- read.csv(“….
>> na.omit(INFAST_Behavioral)
>> INFAST_Behavioral$RT = scale(INFAST_Behavioral$RT, center = TRUE, scale = TRUE)
>> INFAST_Behavioral$PzAlpha = scale(INFAST_Behavioral$PzAlpha, center = TRUE, scale = TRUE)
>> sumModelInteraction <- lmer(RT ~ 1 + (Condition + PzAlpha + Drive + PzAlpha*Condition) + (1 | subject) + (1 | trial), data = INFAST_Behavioral)
>> summary(sumModelInteraction)
>>
>> subject Condition Drive trial FzAlpha CzAlpha PzAlpha FzTheta CzTheta PzTheta FzDelta CzDelta PzDelta RT ACC
>> 1 HumanLanguage 1 1 -1.41 -4.3585 -5.5431 6.1516 1.5911 3.6247 22.38 18.181 13.812 1568.984857 1
>> 1 HumanLanguage 1 2 -7.8605 2.0156 4.7392 15.992 12.122 6.9088 26.861 20.592 16.326 1721.359714 1
>> 1 HumanLanguage 1 3 -2.6982 -5.6067 -10.038 6.285 5.5172 1.2894 13.565 12.981 11.63 1257.092571 1
>> 1 HumanLanguage 1 4 3.3975 4.8789 -1.3249 7.0177 9.6703 6.1539 10.231 12.261 12.485 1559.461429 1
>> …(skipping to Subject 2)
>> 2 HumanLanguage 1 1 1.6791 2.8887 0.28174 -11.387 -9.9352 3.5936 -1.5767 3.9401 6.7201 1302.328857 1
>> 2 HumanLanguage 1 2 -13.284 -8.2603 -6.6124 -5.9373 -8.7551 0.10394 4.5621 10.204 12.261 969.0088571 1
>> 2 HumanLanguage 1 3 -0.048973 1.1329 0.67399 -2.1432 2.5077 -2.4641 9.4667 10.883 7.1396 721.3997143 1
>> 2 HumanLanguage 1 4 5.0779 6.8916 6.3892 -1.8682 3.1637 7.9712 8.0994 10.883 10.975 707.1145714 1
>> 2 HumanLanguage 1 5 -7.0495 -2.782 3.1668 8.4332 10.646 9.3726 -3.5937 -7.3769 5.4472 892.8214286 1
>> 2 HumanLanguage 1 6 -1.462 -8.1223 -6.5896 -10.895 -5.6311 0.39941 7.5473 12.783 14.698 611.8802857 1
>> 2 HumanLanguage 1 7 -2.6402 -5.1213 -3.7372 3.4542 4.2234 -0.99898 1.4089 4.1976 0.56587 761.8742857 1
>> 2 HumanLanguage 1 8 3.4393 4.6302 1.5525 1.4604 3.1716 3.1622 -2.3427 2.908 4.2259 680.9251429 1
>> 2 HumanLanguage 1 9 -0.81024 -0.21642 -2.3876 2.5839 4.7307 1.5441 3.3761 8.4485 12.02 769.0168571 1
>> 2 HumanLanguage 1 10 -6.4045 -4.4937 -2.2449 0.94456 2.7048 0.65565 -1.9791 0.26436 1.8435 885.6788571 1
>>
>> Results:
>>
>> Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [
>> lmerMod]
>> Formula: RT ~ 1 + (Condition + PzAlpha + Drive + PzAlpha * Condition) +
>> (1 | subject) + (1 | trial)
>> Data: INFAST_Behavioral
>>
>> REML criterion at convergence: 3876.4
>>
>> Scaled residuals:
>> Min 1Q Median 3Q Max
>> -3.4308 -0.5227 -0.1194 0.3547 8.4095
>>
>> Random effects:
>> Groups Name Variance Std.Dev.
>> subject (Intercept) 0.580073 0.76163
>> trial (Intercept) 0.004778 0.06912
>> Residual 0.434918 0.65948
>> Number of obs: 1839, groups: subject, 39; trial, 10
>>
>> Fixed effects:
>> Estimate Std. Error df t value Pr(>|t|)
>> (Intercept) -0.27054 0.17607 40.80000 -1.537 0.13213
>> ConditionMachineLang 0.41644 0.24595 36.90000 1.693 0.09884 .
>> PzAlpha 0.01192 0.02411 1797.40000 0.494 0.62117
>> Drive 0.02948 0.01083 1788.40000 2.722 0.00655 **
>> ConditionMachineLanguage:PzAlpha -0.01998 0.03476 1803.10000 -0.575 0.56560
>> ---
>> 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
>>
>>
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>>
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