I have a generalised linear mixed effect model with 5 fixed effects and two by-subject and by-item random intercepts. The outcome variable (Slide4_YesNo) is the participants' responses in a recognition memory task (Old = 1, New = 0). Old_Lure is the condition (i.e. Old, lure) and it is a factor with two levels. The other 4 fixed effect variables are some metrics.
This is my code: model_glmer <- glmer(Slide4_YesNo ~ ZcNOF * Old_Lure + ZcMeanS * Old_Lure + ZcMeanCorStrWithin * Old_Lure + ZcSlope * Old_Lure + (1 | Subject) + (1 | WordCat), family = "binomial", #nAGQ = 0, control = glmerControl(optCtrl=list(maxfun=6e4)), # <- this is the controller, it means running to 60000 times data = pilot)
This is the outcome:
Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.11101 0.11298 -18.68 < 2e-16 *** ZcNOF 0.18156 0.08554 2.12 0.033807 * Old_LureOld 4.00050 0.10043 39.83 < 2e-16 *** ZcMeanS 0.06337 0.09008 0.70 0.481798 ZcMeanCorStrWithin -0.08477 0.08046 -1.05 0.292105 ZcSlope -0.24221 0.07932 -3.05 0.002261 ** ZcNOF:Old_LureOld -0.49962 0.10051 -4.97 6.66e-07 *** Old_LureOld:ZcMeanS -0.30082 0.10894 -2.76 0.005756 ** Old_LureOld:ZcMeanCorStrWithin -0.03581 0.10051 -0.36 0.721625 Old_LureOld:ZcSlope 0.35940 0.09247 3.89 0.000102 ***
I would like to test memory discrimination, in terms of the degree to which the probabiilty of responding old vs lure (slide4_YesNo, 1 or 0), depends on whether items are old or lure (Old_Lure, condition) when it interacts with some continuous metrics. So I am interested in the interactions (last four results). Basically I want to see the difference between conditions when holding the continuous variables. I guess I need some contrasts here, but I do not understand how to do them with multiple continuous variables. Any idea? Thank you