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Scope

bru_reg() is scoped to inlabru used as a fast Bayesian GLM: models with fixed effects and IID random intercepts only. If your model includes any of the following, stop here and use inlabru’s native interface instead:

• Spatial random effects: BYM2, SPDE, LGCP
• Structured temporal effects: AR1, RW1, RW2
• Custom nonlinear predictors with mapper functions

For those models, cross-validation RMSE is not a meaningful quantity and the parsnip abstraction does not hold. See vignette("scoring") for the statistically appropriate evaluation approach.

For simple Bayesian GLMs — Poisson regression for counts, Gaussian regression for continuous outcomes, binomial for proportions — the parsnip layer gives you a consistent interface for comparison with frequentist or ML baselines and clean integration with the rest of the tidymodels ecosystem.

Setup

library(inlabru)
library(inlaops)
library(parsnip)
library(broom)
library(rsample)
library(yardstick)
library(dplyr)

Example: Poisson regression for count data

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))

bru_reg() does not auto-encode factor or character predictors — fitting counts ~ Intercept(1) + habitat on a raw factor or character column errors with a pointer to three workarounds: pre-encode via recipes::step_dummy() + recipes::bake() before calling fit() (recipes/workflows cannot be wrapped around bru_reg because they rebuild formulas without inlabru’s component syntax), convert to integer indicator columns yourself, or use inlabru’s native habitat(habitat, model = "factor_full") component syntax.

Fit with bru_reg()

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

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

fit

The engine automatically sets control.compute = list(config = TRUE, dic = TRUE, waic = TRUE). The fit is serving-ready for the vetiver bridge without any extra options. For large data sets where WAIC is too expensive, opt out via set_engine("inlabru", dic = FALSE, waic = FALSE); config = TRUE is always set because the vetiver /predict endpoint needs it.

Inspect with broom

The tidy(), glance(), and augment() methods for bru fits come from inlabru (>= 2.15.0).

tidy(fit$fit)

glance(fit$fit)

Predict

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

The parsnip predict() returns the posterior mean on the response scale. For predictive uncertainty, use augment() directly:

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

Comparing against a frequentist baseline

A side-by-side comparison with a frequentist GLM is a one-liner per engine. Note that bru_reg requires inlabru component syntax (Intercept(1) is explicit), while glm() uses the standard implicit-intercept form — the formulas differ on purpose.

# 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)

recipes and workflows are not compatible with bru_reg because they rebuild formulas in the standard implicit-intercept shape, which bru() rejects (it needs the explicit Intercept(1) component). For preprocessing, manipulate data before the call to fit() rather than wrapping in a recipe.

Cross-validation with bru_cv_predict()

For fixed-effects models, bru_cv_predict() wraps vfold_cv() and returns a tidy holdout prediction tibble:

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)

For proper scoring rules that use the full posterior distribution rather than just the mean, see vignette("scoring").

Connecting to vetiver

A model fit via bru_reg() is automatically serving-ready:

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)

See vignette("vetiver") for the full serving and monitoring workflow.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.