I have a group of participants (Subj) all of whom have a covariate measure (Pars). Each participant saw a series of images (Image) that belonged to one of three categories (Stim). To each image, participants made a response (Resp) that was classified as a behavior (Beh). I have combined Stim and Beh (StimBeh), and for each of these Stimulus-Behaviors I have a parameter estimate (Est). I would like to see what the effect of the covariate is in the parameter estimate for the different types of behaviors associated with the different stimuli types, when treating subject and image as random effects. I believe StimBeh should be a fixed effect. I have been unable to make the model converge.
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Combining Stim and Beh is a strange approach. I may be missing something, but I would have incorporated Stim and Beh (or possibly Resp) as covariates. Combining them imposes an interaction without allowing each in the model as a main effect. Finally, if your parameter lies in $[0,1]$, you might want to transform the response.
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$\begingroup$ Ah of course. I have updated that. Without covariates the model runs fine, but it still will not converge with covariates. I tried increasing the number of iterations to no avail. Should I conclude that I don't have enough data for the model complexity? $\endgroup$ Commented Sep 4, 2020 at 17:52
(1 | Subj)rather than(Pars | Subj)converge ? Also please don't post a picture of output, Post the output itself. How many subjects and images do you have ? And why do you appear to be subsetting the data based on values of the outcome ? $\endgroup$