eval_pred_dens: Evaluate Predictive Densities

In shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors

Evaluate Predictive Densities

Description

eval_pred_dens evaluates the predictive density for a set of points based on a fitted shrinkGPR model.

Usage

eval_pred_dens(x, mod, data_test, nsamp = 100, log = FALSE)

Arguments

x	Numeric vector of points for which the predictive density is to be evaluated.
mod	A shrinkGPR object representing the fitted Gaussian process regression model.
data_test	Data frame with one row containing the covariates for the test set. Variables in data_test must match those used in model fitting.
nsamp	Positive integer specifying the number of posterior samples to use for the evaluation. Default is 100.
log	Logical; if TRUE, returns the log predictive density. Default is FALSE.

Details

This function computes predictive densities by marginalizing over posterior samples drawn from the fitted model. If the mean equation is included in the model, the corresponding covariates are incorporated.

Value

A numeric vector containing the predictive densities (or log predictive densities) for the points in x.

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)

  # Create point at which to evaluate predictive density
  data_test <- data.frame(x1 = 0.8, x2 = 0.5)
  eval_points <- c(-1.2, -1, 0)

  eval_pred_dens(eval_points, res, data_test)

  # Is vectorized, can also be used in functions like curve
  curve(eval_pred_dens(x, res, data_test), from = -1.5, to = -0.5)
  abline(v = sin(2 * pi * 0.8), col = "red")
  }

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