bayesics (version 2.0.2)

predict.lm_b_bma: Predict method for bma model fits

Description

Predict method for bma model fits

Usage

# S3 method for lm_b_bma
predict(object, newdata, CI_level = 0.95, PI_level = 0.95, seed = 1, ...)

Value

list.

• newdata tibble with estimate, prediction intervals, and credible intervals for the mean.

• posterior_draws

  • mean_of_ynew draws of \(E(y)\), marginalizing out the model

  • posterior draws of ynew

Arguments

object

Object of class bma

newdata

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

CI_level

Posterior probability covered by credible interval

PI_level

Posterior probability covered by prediction interval

seed

integer. Always set your seed!!!

...

optional arguments.

Examples

# \donttest{
# Create data
set.seed(2025)
N = 500
test_data = 
  data.frame(x1 = rnorm(N),
             x2 = rnorm(N),
             x3 = letters[1:5],
             x4 = rnorm(N),
             x5 = rnorm(N),
             x6 = rnorm(N),
             x7 = rnorm(N),
             x8 = rnorm(N),
             x9 = rnorm(N),
             x10 = rnorm(N))
test_data$outcome = 
  rnorm(N,-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")) )

# Fit linear model using Bayesian model averaging
fit <-
  bma_inference(outcome ~ .,
                test_data,
                user.int = FALSE)
predict(fit)
# }