Reproducible example for Loo error

LooErrorExample.R

## Load libraries
pacman::p_load(tidyverse,brms)

## Load data
data <- read_csv("data.csv")


## Define formulas
Model1_f <- bf(Outcome ~ Diagnosis + (1|ID))
Model2_f <- bf(Outcome ~ Diagnosis + (1|p|ID), 
               sigma ~ Diagnosis + (1|p|ID))

# Define priors
SkepticPrior1 <- c(
  prior(normal(0,.05), class=Intercept),
  prior(normal(0,.05), class=b),
  prior(normal(0,.01), class=sd)
)

SkepticPrior2 <- c(
  prior(normal(0,.05), class=Intercept),
  prior(normal(0,.05), class=b),
  prior(normal(0,.01), class=sd),
  prior(lkj(5), class=cor),
  prior(normal(0,.05), class=Intercept, dpar = sigma),
  prior(normal(0,.05), class=b, dpar = sigma),
  prior(normal(0,.01), class=sd, dpar = sigma)
)


# Run models

m1 <- brm(Model1_f, 
         data = data,
         family = gaussian,
         iter = 2000, 
         chains = 2, 
         cores = 2,
         prior = SkepticPrior1,
         sample_prior = T,
         file = "Model1",
         control= list(adapt_delta = 0.99,
                       max_treedepth=20))

m1 <- add_criterion(m1, criterion=c("loo"), reloo=F, file = "Model1")

m2 <- brm(Model2_f, 
          data = data,
          family = gaussian,
          iter = 2000, 
          chains = 2, 
          cores = 2,
          prior = SkepticPrior1,
          sample_prior = T,
          file = "Model2",
          control= list(adapt_delta = 0.99,
                        max_treedepth=20))

m2 <- add_criterion(m1, criterion=c("loo"), reloo=F, file = "Model2")

# Compare, when things stop working
w <- loo_model_weights(m1,m2)