bayesics (version 2.1.0)

predict.glm_b: Predict method for glm_b model fits

Description

Predict method for glm_b model fits

Usage

# S3 method for glm_b
predict(
  object,
  newdata,
  trials,
  CI_level = 0.95,
  PI_level = 0.95,
  seed = 1,
  n_draws = 5000,
  ...
)

Value

tibble with estimate (posterior mean), prediction intervals, and credible intervals for the mean.

Arguments

object

Object of class glm_b

newdata

An optional data.frame in which to look for variables with which to predict.

trials

Integer vector giving the number of trials for each observation if family = binomial().

CI_level

Posterior probability covered by credible interval

PI_level

Posterior probability covered by prediction interval

seed

integer. Always set your seed!!!

n_draws

integer. Number of posterior draws used for prediction

...

optional arguments.

Examples

# \donttest{
set.seed(2025)
N = 500
test_data =
  data.frame(x1 = rnorm(N),
             x2 = rnorm(N),
             x3 = letters[1:5],
             time = rexp(N))
test_data$outcome =
  rnbinom(N,
          mu = exp(-2 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e"))) * test_data$time,
          size = 0.7)

# Fit using variational Bayes (default)
fit_vb1 <-
  glm_b(outcome ~ x1 + x2 + x3 + offset(log(time)),
        data = test_data,
        family = negbinom(),
        seed = 2025)
# Predict
predict(fit_vb1)
# }