[R-sig-ME] comparing 2 models

Voeten, C.C. c.c.voeten at hum.leidenuniv.nl
Wed Apr 25 09:19:57 CEST 2018 

Hi Pam,

I don't know if it is the 'best' way, but 'a' way could be to simply compare the AICs of these two models?

lmer(RT ~ 1 + PzAlpha+ HRV+lnX+Drive + PzAlpha:Drive +  (1 | Pnum) + (1 | Trial), data = INFAST_Behavioralnew, REML = FALSE)
lmer(RT ~ 1 + PzAlpha+ HRV+lnX+Drive + HRV:Drive +  (1 | Pnum) + (1 | Trial), data = INFAST_Behavioralnew, REML = FALSE)

Best,
Cesko

-----Oorspronkelijk bericht-----
Van: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> Namens P Greenwood
Verzonden: woensdag 25 april 2018 03:18
Aan: r-sig-mixed-models at r-project.org
Onderwerp: [R-sig-ME] comparing 2 models

Hello

Although alpha-band EEG is something of a gold standard in predicting processing of stimuli (pre-stimulus alpha), it is not easy to measure EEG outside the lab (e.g. in a vehicle).  In contrast, heart-rate and eye gaze are more reliably obtained in the field using low-cost wearable sensors. 

We sought to compare the ability of 3 sensors to model human reaction time (RT) to a signal from automation.  We compared these measures: alpha-band (PzAlpha), heart-rate variability (HRV), and eye gaze (lnX).  Each person has 10 trials in each of 5 drives. Pnum is subject number.

We sought to model RT from  alpha-band,  heart-rate variability (HRV), and eye gaze (lnX) and Drive.

sumModelInteraction4 <- lmer(RT ~ 1 + (PzAlpha+ HRV+lnX+Drive)^3 +  (1 | Pnum) + (1 | Trial), data = INFAST_Behavioralnew, REML = FALSE)

The output, pasted below, reveals interactions: PzAlpha x HRV, PzAlpha x Drive, and HRV x Drive. Also pasted below is some of the raw data.

An LRT comparing this model with an additive model (PzAlpha+RMSSD+lnX+Drive+Trial) yields a significant difference.  This suggests that the interactions  PzAlpha x Drive and HRV x Drive are meaningful predictors. 

We would like to determine whether PzAlpha x Drive or  HRV x Drive is the better predictor of RT.  What is the best way to compare those 2 models?  The measures are scaled and centered.

Thank you very much.

Pam Greenwood

Fixed effects:
                      Estimate Std. Error         df t value Pr(>|t|)   
(Intercept)         -1.908e-01  1.368e-01  3.310e+01  -1.395  0.17235   
PzAlpha             -1.098e-01  6.042e-02  2.339e+02  -1.818  0.07041 . 
HRV               -1.127e-01  6.402e-02  1.112e+03  -1.760  0.07868 . 
lnX                  7.921e-02  7.547e-02  1.102e+03   1.050  0.29416   
Drive                5.382e-02  1.661e-02  1.080e+03   3.241  0.00123 **
PzAlpha: HRV       -1.586e-01  6.424e-02  7.035e+02  -2.468  0.01382 * 
PzAlpha:lnX          1.116e-01  7.400e-02  8.456e+02   1.508  0.13199   
PzAlpha:Drive        4.405e-02  1.730e-02  1.049e+03   2.546  0.01103 * 
HRV:lnX           -4.723e-02  6.905e-02  1.100e+03  -0.684  0.49407   
HRV:Drive          3.652e-02  1.747e-02  1.098e+03   2.091  0.03677 * 
lnX:Drive           -1.275e-02  1.992e-02  1.100e+03  -0.640  0.52217   
PzAlpha: HRV:lnX    1.473e-02  2.567e-02  8.244e+02   0.574  0.56621   
PzAlpha: HRV:Drive  3.308e-02  1.878e-02  1.084e+03   1.761  0.07845 . 
PzAlpha:lnX:Drive   -2.325e-02  2.005e-02  9.166e+02  -1.160  0.24640   
HRV:lnX:Drive      9.729e-03  1.769e-02  1.097e+03   0.550  0.58247   


	
	Pnum
	Drive		Trial	RT	ACC	FzAlpha	CzAlpha	PzAlpha	FzTheta	CzTheta	PzTheta	MeanPupil	lnX	lnY	MeanRR	
HRV
20	1	1	1480.8931	1	7.9928	10.216	7.6254	3.4916	4.8657	6.4977	4.280969072	-3.208115816	-2.423813328	0.7336  		0.074666667
20	1	2	1983.254	1	-8.2609	0.62018	0.32812	4.2257	6.0181	6.5564	4.360414101	-1.926558582	-2.364252526	0.7336		0.074666667
20	1	3	1588.0317	1	1.2572	5.5394	9.0619	4.322	6.7421	7.2778	4.429370379	-2.510514134	-2.890876402	0.734		0.073333333
20	1	4	2600	0	-2.0822	-3.5216	2.597	7.6632	9.4505	9.2404	3.994177574	-2.121340179	-3.875411777	0.7408		0.050666667
20	1	5	1268.9969	1	2.463	4.5837	4.0916	3.4363	4.2989	-2.1573	3.927884406	-1.754642861	-2.737213207	0.7516		0.014666667




















































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