## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  # Evaluated wherever INLA and the packages used here are installed.
  eval     = all(vapply(
    c("INLA", "parsnip", "broom", "rsample", "yardstick", "dplyr", "vetiver", "pins"),
    requireNamespace, logical(1), quietly = TRUE
  ))
)


## ----message = FALSE----------------------------------------------------------
library(inlabru)
library(inlaops)
library(parsnip)
library(broom)
library(rsample)
library(yardstick)
library(dplyr)


## -----------------------------------------------------------------------------
set.seed(123)
n   <- 300
dat <- data.frame(
  area   = rnorm(n),
  effort = runif(n, 0.5, 2.0)
)
log_mu     <- 0.5 + 0.8 * dat$area + log(dat$effort)
dat$counts <- rpois(n, exp(log_mu))


## -----------------------------------------------------------------------------
spec <- bru_reg(mode = "regression", family = "poisson")

fit <- spec |>
  set_engine("inlabru") |>
  fit(counts ~ Intercept(1) + area + effort, data = dat)

fit


## -----------------------------------------------------------------------------
tidy(fit$fit)

glance(fit$fit)


## -----------------------------------------------------------------------------
new_dat <- data.frame(area = c(-1, 0, 1), effort = c(1, 1, 1))
predict(fit, new_data = new_dat)


## -----------------------------------------------------------------------------
augment(fit$fit, data = new_dat, pred_formula = ~ exp(Intercept + area + effort))


## -----------------------------------------------------------------------------
# Frequentist baseline
fit_glm <- glm(counts ~ area + effort, data = dat, family = poisson())

# Bayesian with inlabru, via parsnip
bru_spec <- bru_reg(family = "poisson") |> set_engine("inlabru")
fit_bru  <- bru_spec |>
  fit(counts ~ Intercept(1) + area + effort, data = dat)

# Coefficient comparison (response-scale interval for the Bayesian fit)
broom::tidy(fit_glm)
broom::tidy(fit_bru$fit)


## -----------------------------------------------------------------------------
cv_preds <- bru_cv_predict(
  formula      = counts ~ Intercept(1) + area + effort,
  family       = "poisson",
  data         = dat,
  v            = 5L,
  pred_formula = ~ exp(Intercept + area + effort),
  n_samples    = 500L
)

cv_preds

# Compute RMSE across folds
yardstick::rmse(cv_preds, truth = .truth, estimate = .pred)


## -----------------------------------------------------------------------------
library(vetiver)
library(pins)

v <- vetiver_model(fit$fit, "poisson_glm")
v$metadata$user <- list(
  pred_formula = "~ exp(Intercept + area + effort)",
  n_samples    = 1000L
)

board <- board_temp()
vetiver_pin_write(board, v)