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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