predictSLGP_quantiles: Predict quantiles from a SLGP model at new locations

In SLGP: Spatial Logistic Gaussian Process for Field Density Estimation

Predict quantiles from a SLGP model at new locations

Description

Computes quantile values at specified levels (probs) for new covariate points, based on the posterior CDFs from a trained SLGP model.

Usage

predictSLGP_quantiles(
  SLGPmodel,
  newNodes,
  probs,
  interpolateBasisFun = "WNN",
  nIntegral = 101,
  nDiscret = 101
)

Arguments

SLGPmodel	An object of class SLGP-class.
newNodes	A data frame of covariate values.
probs	Numeric vector of quantile levels to compute (e.g., 0.1, 0.5, 0.9).
interpolateBasisFun	Character string specifying interpolation scheme: "nothing", "NN", or "WNN" (default).
nIntegral	Number of integration points for computing the SLGP outputs.
nDiscret	Discretization level of the response axis (for CDF inversion).

Value

A data frame with columns:

• The covariates in newNodes (repeated per quantile level),

• A column probs indicating the quantile level,

• Columns qSLGP_1, qSLGP_2, ... for each posterior sample's quantile estimate.

Examples

# Load Boston housing dataset
library(MASS)
data("Boston")
# Set input and output ranges manually (you can also use range(Boston$age), etc.)
range_x <- c(0, 100)
range_response <- c(0, 50)

# Train an SLGP model using Laplace estimation and RFF basis
modelLaplace <- slgp(medv ~ age,        # Use a formula to specify response and covariates
                 data = Boston,     # Use the original Boston housing data
                 method = "Laplace",    # Train using Maximum A Posteriori estimation
                 basisFunctionsUsed = "RFF",         # Random Fourier Features
                 sigmaEstimationMethod = "heuristic",  # Auto-tune sigma2 (more stable)
                 predictorsLower = range_x[1],         # Lower bound for 'age'
                 predictorsUpper = range_x[2],         # Upper bound for 'age'
                 responseRange = range_response,       # Range for 'medv'
                 opts_BasisFun = list(nFreq = 200,     # Use 200 Fourier features
                                      MatParam = 5/2), # Matern 5/2 kernel
                 seed = 1)                             # Reproducibility
dfX <- data.frame(age=seq(range_x[1], range_x[2], 1))
# Predict some quantiles, for instance here the first quartile, median, third quartile
predQuartiles <- predictSLGP_quantiles(SLGPmodel= modelLaplace,
                                       newNodes = dfX,
                                       probs=c(0.25, 0.50, 0.75))

SLGP documentation built on Sept. 9, 2025, 5:25 p.m.