[R-sig-ME] subjects within groups and effects of group

P Greenwood pgreenw1 at gmu.edu
Fri Jan 19 14:09:33 CET 2018


Thanks to you both.

Trial refers to stimulus events.  The stimuli are the same on each Trial, although the order of the Trials varies between Drives.   But, yes, Trial is a sequence number for the repetition so that there could be some adaptation or change in response related to number of exposures.  (Assuming that is what you meant).  How would I include Trial as a continuous fixed effect?

If the effect of Condition were “significant.” how would one decompose that to examine each group (Condition) separately?

Regards

Pam


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 Jan 19, 2018, at 5:58 AM, Phillip Alday <phillip.alday at mpi.nl> wrote:
> 
> 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
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>> thierry.onkelinx at inbo.be
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>> 
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>> able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
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>> The combination of some data and an aching desire for an answer does
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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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