predict.shrinkGPR: Generate Predictions

In shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors

Generate Predictions

Description

predict.shrinkGPR generates posterior predictive samples from a fitted shrinkGPR model at specified covariates.

Usage

## S3 method for class 'shrinkGPR'
predict(object, newdata, nsamp = 100, ...)

Arguments

object	A shrinkGPR object representing the fitted Gaussian process regression model.
newdata	Optional data frame containing the covariates for the prediction points. If missing, the training data is used.
nsamp	Positive integer specifying the number of posterior samples to generate. Default is 100.
...	Currently ignored.

Details

This function generates predictions by sampling from the posterior predictive distribution. If the mean equation is included in the model, the corresponding covariates are incorporated.

Value

A matrix containing posterior predictive samples for each covariate combination in newdata.

Examples

if (torch::torch_is_installed()) {
  # Simulate data
  set.seed(123)
  torch::torch_manual_seed(123)
  n <- 100
  x <- matrix(runif(n * 2), n, 2)
  y <- sin(2 * pi * x[, 1]) + rnorm(n, sd = 0.1)
  data <- data.frame(y = y, x1 = x[, 1], x2 = x[, 2])

  # Fit GPR model
  res <- shrinkGPR(y ~ x1 + x2, data = data)
  # Example usage for in-sample prediction
  preds <- predict(res)

  # Example usage for out-of-sample prediction
  newdata <- data.frame(x1 = runif(10), x2 = runif(10))
  preds <- predict(res, newdata = newdata)
  }

shrinkGPR documentation built on April 4, 2025, 3:07 a.m.