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

Thu Jan 25 23:44:57 CET 2018

Completely agree with Thierry here.

In addition to the usual considerations about the bias-variance tradeoff and partial pooling, you need to have things in one model if you really want to compare them. The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant (Gelman and Stern 2012, doi:10.1198/000313006X152649<https://doi.org/10.1198/000313006X152649>), so if you care about the significance of the difference, then you need to actually test the difference!

For your other question

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?

I would use slightly different names to make things clear. Separate 'Trial' (a fixed series of stimuli) from SeqNo (the sequential position of a given Trial within a Drive).

Then your model looks something like this:

lmer(RT ~ 1 + Condition*PzAlpha + Drive + SeqNo + (1 | subject) + (1 | trial)

I've left out any interactions there, but I suspect you'll at least have an interaction with alpha and sequence number -- I imagine that later trials (i.e. higher sequence numbers) will have worse RTs (exhaustion effects) as will trials with higher alpha power and that this two effects will enhance each other.

Including sequence information in the model has received some attention in the psycholinguistic as well as the broader psychology literature as a way of controlling for adapt ion effects. GAMMs have been proposed for such cases to allow for non linear adaptation effects, but I wouldn't mess around with that until you feel much more comfortable with the standard LMMs.

And of course, if SeqNo doesn't improve model fit, you can simply omit it for parsimony and easy of both interpretation and fitting.

Phillip

On 25/01/18 17:33, Thierry Onkelinx wrote:

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 at inbo.be<mailto:thierry.onkelinx at inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be<http://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 at gmu.edu><mailto:pgreenw1 at 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 at gmu.edu<mailto:Pgreenw1 at gmu.edu>
http://psychology.gmu.edu/people/pgreenw1

On Jan 19, 2018, at 8:09 AM, P Greenwood <pgreenw1 at gmu.edu><mailto:pgreenw1 at 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 at gmu.edu<mailto: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><mailto: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
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be<mailto:thierry.onkelinx at inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be<http://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 at gmu.edu><mailto: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<mailto:Pgreenw1 at gmu.edu>
http://psychology.gmu.edu/people/pgreenw1

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