[R-sig-ME] subjects within groups and effects of group
Thierry Onkelinx
thierry.onkelinx at inbo.be
Thu Jan 25 17:33:28 CET 2018
Dear Pam,
I'd probably combine both datasets in a single analysis.
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 op inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////
2018-01-24 14:02 GMT+01:00 P Greenwood <pgreenw1 op gmu.edu>:
> Dear Drs Alday and Onkelinx
>
> I wondered if you had thoughts on the best way to conduct followup analysis
> of the between-subjects Condition to which people were randomly assigned.
>
> Pam Greenwood
>
> 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 op gmu.edu
> http://psychology.gmu.edu/people/pgreenw1
>
> On Jan 19, 2018, at 8:09 AM, P Greenwood <pgreenw1 op gmu.edu> wrote:
>
> 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 op gmu.edu
> http://psychology.gmu.edu/people/pgreenw1
>
> On Jan 19, 2018, at 5:58 AM, Phillip Alday <phillip.alday op 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
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx op inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be
>
> ///////////////////////////////////////////////////////////////////////////////////////////
> To call in the statistician after the experiment is done may be no
> more than asking him to perform a post-mortem examination: he may be
> able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does
> not ensure that a reasonable answer can be extracted from a given body
> of data. ~ John Tukey
> ///////////////////////////////////////////////////////////////////////////////////////////
>
>
>
>
> 2018-01-18 18:51 GMT+01:00 P Greenwood <pgreenw1 op 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 op gmu.edu
> http://psychology.gmu.edu/people/pgreenw1
>
>
> [[alternative HTML version deleted]]
>
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