Thank you very much for your responses!
What if I reduce the model to
modelLSI3 <- lmer(SA ~ Index1* LSI+ (1+LSI |ID),data = LSIDATA, control =
lmerControl(optimizer ="bobyqa"), REML=TRUE).
This would allow me to see the random effects of LSI and I can drop the
random effect of age (Index1) sinc
I am running a multilevel growth curve model to examine predictors of
social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a
continuous numeric variable. The age variable (Index1) has been coded as 0
for age 12, 1 for age 15 and 2 for age 18. I am currently using a time
varying predic
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