Syntax and Divergent Transitions in Binomial Models using R

Modeling
,
1

Hi all,

I’m aiming to model binary species traits (presence = 1, absence = 0) as a function of some sampling variables. So I built a brms model adding to it a phylogenetic structure, and a Gaussian process, gp(), to handle spatial autocorrelation. Here is my data, and the model:

full_model <- brms::brm(male_head | trials(1 + 0) ~ 
           PC1 + PC2 + PC3 +
           gp(x_coords, y_coords, k = 10, c = 5/4, iso = F, scale = FALSE) + 
           (1|gr(phylo, cov = covariance_matrix)),
           data = data,
           family = binomial(),
           prior = prior,
           data2 = list(covariance_matrix = covariance_matrix),
           backend = "cmdstanr",
           cores = 4,
           control=list(adapt_delta = 0.999, 
                        max_treedepth = 15, 
                        step_size  = 0.001),
           thin = 10, 
           iter = 6000, 
           warmup = 2000, 
           seed = 123)

Priors consist of flat, uninformative values which I obtained through get_prior().

prior <- get_prior(male_hindwing  | trials(1+0) ~
                   PC1 + PC2 + PC3, 
                   data = data,
                   family = binomial())

So, each line of my data represents a observation with a binary response variable (i.e., a 1 or 0).
First of all, I am in doubt regarding the arguments to be used in trials(). As I have species which have and species which do not have the traits I am modelling I thought I could use trials(1 + 0) (I saw a vignette pointing it out, but I lost it). Is the syntax correct?

Additionally, the model is having a lot of divergent errors. I already used (1) different values for control parameters, (2) changed brms_algorithm from NUTS to HMC, and (3) reduced and scaled sampling variables, but no success, I keep getting several divergent transitions.
Any guess on how to deal with it?

My sessionInfo():

other attached packages:
[1] cmdstanr_0.5.3 mvSLOUCH_2.7.5 abind_1.4-5 phytools_1.2-0 maps_3.4.0 ape_5.6-2
[7] dplyr_1.0.10 brms_2.18.0 Rcpp_1.0.10