Hi, I am trying to fit a non-linear formula model, specifically a hyperbolic function for delayed discounting using hiearchical model.

I want to estimate logk with a covariate (factor variable with 9 levels), but it keeps giving me an error message that MCMC fails to find an initial value for the chain. Variables used in this formula are all numeric excpet for `subject`

and `choice_type_detail`

. When I remove choice_type for estimating logk, the model has no fitting issues. It converged well. Can you guys share your wisdom how to fix this issue? Thank you so much!

```
formula_cross <- bf(
choice ~ 0.5 + 0.5 * inv_logit(beta * (EVlater - EVsooner)),
nlf(EVlater ~ log_amount_later / (1 + exp(logk) * delay_later_years)),
nlf(EVsooner ~ log_amount_sooner / (1 + exp(logk) * delay_sooner_years)),
logk ~ 0 + choice_type_detail + (1|subject),
beta ~ 1 + (1|subject),
nl = TRUE
)
priors_cross_commodity <- c(
prior(normal(-4, 2), class = "b", nlpar = "logk"),
prior(normal(0, 1), class = "b", nlpar = "beta"),
prior(normal(0, 0.5), class = "sd", nlpar = "logk"),
prior(normal(0, 0.5), class = "sd", nlpar = "beta")
)
set_cmdstan_path(path = "C:/Users/jdi94/.cmdstan/cmdstan-2.35.0")
options(brms.backend = "cmdstanr")
fit_cross_comm_full <- brm(
formula,
data = full_df,
family = bernoulli(),
prior = priors_cross_commodity,
chains = 4,
cores = 4,
iter = 4000,
warmup = 2000,
control = list(adapt_delta = 0.99, max_treedepth = 15)
)
```