shrinkGPR (version 1.0.0)

predict.shrinkGPR: Generate Predictions

Description

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

Usage

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

Value

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

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.

Examples

# \donttest{
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)
  }
# }