Hello, I am using brms to fit a cumulative model. I have one model whose dependent measure is a 5 point likert scale and one model whose dependent measure is the average of 3 questions. (see code below). I am getting a divergent transition warning, wondering if there is anything I can do to solve this (e.g., should i use a different family?). I have tried upping chains to 6 and iterations to 10,000 but it didn’t solve the problem. The problem also gets worse when I run null models and take out the parameters.

**priors** = c(prior(normal(-0.97, 1), class = Intercept, coef = 1),

prior(normal(-0.43, 1), class = Intercept, coef = 2),

prior(normal( 0.00, 1), class = Intercept, coef = 3),

prior(normal( 0.43, 1), class = Intercept, coef = 4))

#This is the data:

Test_data_S2.csv (171.5 KB)

#This is the model:

fit_reasons_Mat ← brm(formula = plan_materials ~ reason_prestige+reason_lab+reason_require+reason_public+other_materials_cooper+self_materials_cooper+Identify+LogYears,

TEST_data_S2,

family = cumulative(“probit”),

save_all_pars = TRUE,

init = 1000,

prior=priors

)

“Warning: There were 15 divergent transitions after warmup”. edited to add, I also get this before the model runs "The global prior ‘student_t(3, 0, 2.5)’ of class ‘Intercept’ will not be used in the model as all related coefficients have individual priors already. If you did not set those priors yourself, then maybe brms has assigned default priors. "