Error when predicting and plotting from brm model

Interfaces brms
3

Hello,

None of those apply to my data. I have been trying to solve this using code I found in this post, which I understand did work in the past for the poster hmeleiro. Here’s the dataset for it too.
spain_data.csv (21.0 KB)

library(tidyverse)
library(brms)

raw <- read_csv("spain_data.csv")

formula <- casos ~ A * exp( -exp( -(k * (days - delay) ) ) )

form_mult <- bf(formula,
                A ~ 1 + (1 | ccaa),
                k ~ 1 + (1 | ccaa),
                delay ~ 1 + (1 | ccaa), 
                nl = TRUE)

priors <- c(
  prior(normal(0, 13000), nlpar = "A", lb=200),
  prior(normal(.1, .05), nlpar = "k", lb=0),
  prior(normal(5, 20), nlpar = "delay", lb=0),
  prior(student_t(3, 0, 5000), class = "sigma"),
  prior(student_t(4, 3000, 5000), class = "sd", group = "ccaa", nlpar = "A"),
  prior(student_t(4, 0, 0.03), class = "sd", group = "ccaa", nlpar = "k")
)

mod <- brm(form_mult, 
           data = raw,
           prior = priors, 
           seed = 1234,
           family = gaussian("identity"),
           iter = 4000,
           chains = 4, 
           cores=4, 
           sample_prior = "no", 
           control = list(adapt_delta = 0.99,
                          max_treedepth = 12))

# Fitted
pred <- as.data.frame(predict(mod, probs = c(0.01, 0.99), re_formula = NULL))

colnames(pred)[3] <- "low"
colnames(pred)[4] <- "upp"
pred$ccaa <-  raw$ccaa
pred$days <- raw$days
pred$observed <- raw$casos
pred$pob <- raw$pob
pred$low[pred$low < 0] <- 0

pred %>% 
  ggplot(aes(x = days, y = observed)) + 
  geom_point(aes(color = dentro), size = 0.4) +
  geom_ribbon(aes(ymin = low, ymax = upp ), alpha = 0.3) +
  facet_wrap(~ccaa, scales = "free_y")

The error happens when I call the predict function:

Error in tcrossprod(b, X) : "tcrossprod" is not a BUILTIN function
Error: Something went wrong (see the error message above). Perhaps you transformed numeric variables to factors or vice versa within the model formula? If yes, please convert your variables beforehand. Or did you set a predictor variable to NA?