predictSLGP_cdf: Predict cumulative distribution values at new locations using...

In SLGP: Spatial Logistic Gaussian Process for Field Density Estimation

Predict cumulative distribution values at new locations using a SLGP model

Description

Computes the posterior cumulative distribution function (CDF) values at specified covariate values using a fitted SLGP model.

Usage

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

Arguments

SLGPmodel	An object of class SLGP-class.
newNodes	A data frame with covariate values where the SLGP should be evaluated.
interpolateBasisFun	Character string indicating the interpolation scheme for basis functions: one of "nothing", "NN", or "WNN" (default).
nIntegral	Number of integration points along the response axis.
nDiscret	Discretization resolution for interpolation (optional).

Value

A data frame with newNodes and predicted CDF values, columns named cdf_1, cdf_2, ...

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)

#' #Create a SLGP model but don't fit it
modelPrior <- slgp(medv ~ age,        # Use a formula to specify response and covariates
                 data = Boston,     # Use the original Boston housing data
                 method = "none",    # No training
                 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

#Let us make 3 draws from the prior
nrep <- 3
set.seed(8)
p <- ncol(modelPrior@coefficients)
modelPrior@coefficients <- matrix(rnorm(n=nrep*p), nrow=nrep)

# Where to predict the field of pdfs ?
dfGrid <- data.frame(expand.grid(seq(range_x[1], range_x[2], 5),
seq(range_response[1], range_response[2],, 101)))
colnames(dfGrid) <- c("age", "medv")
predPriorcdf <- predictSLGP_cdf(SLGPmodel=modelPrior,
                                newNodes = dfGrid)

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