The “posterior_predict” Function in R

• Package: rstanarm

• Purpose: Generates posterior predictive samples from a fitted model.

• General class: Statistical modeling

• Required argument(s):

• object: The fitted model object.

• Notable optional arguments:

• newdata: New data for prediction. If not provided, predictions are based on the original data.

• draws: Number of posterior samples to generate.

• ...: Additional arguments controlling the prediction process.

• Example:

• # Load the rstanarm package
  library(rstanarm)
  
  # Library psych for the describe function
  library(psych)
  
  # Fit a Bayesian linear regression model
  x = rnorm(1000)
  y = 2*x + 1 + rnorm(1000) # y ~ 2x + 1 + N(0,1)
  data <- data.frame(x,y)
  
  model <- stan_glm(y ~ x, data = data)
  summary(model)
  
  # Generate 1000 posterior predictive samples
  posterior_samples <- posterior_predict(model, newdata = data.frame(x = 1:10), draws = 1000)
  
  # Posterior samples with vars being the input value of x, and the other stats regarding the value of y
  describe(posterior_samples, omit = TRUE, ranges = FALSE, skew = FALSE, quant = c(0.25,0.5,0.75))

• This example demonstrates how to use the posterior_predict function from the rstanarm package to generate posterior predictive samples from a Bayesian linear regression model (model). Optional arguments like newdata can be used to generate predictions for new data and draws controls the number of samples to generate. Additional control over the prediction process can be achieved with other optional arguments. The generated posterior samples can be summarized and visualized for further analysis.