SLGP (version 1.0.1)

predictSLGP_newNode: Predict densities at new covariate locations using a given SLGP model

Description

Computes the posterior predictive probability densities at new covariate points using a fitted Spatial Logistic Gaussian Process (SLGP) model.

Usage

predictSLGP_newNode(
  SLGPmodel,
  newNodes,
  interpolateBasisFun = "WNN",
  nIntegral = 101,
  nDiscret = 101,
  normalise = TRUE
)

Value

A data frame combining newNodes with columns named pdf_1, pdf_2, ..., representing the posterior predictive density for each sample of the SLGP.

Arguments

SLGPmodel

An object of class SLGP-class.

newNodes

A data frame containing new covariate values at which to evaluate the SLGP.

interpolateBasisFun

Character string indicating how basis functions are evaluated: one of "nothing", "NN", or "WNN" (default).

nIntegral

Integer specifying the number of quadrature points over the response space.

nDiscret

Integer specifying the discretization step for interpolation (only used if applicable).

normalise

Boolean, indicates if we return normalised or unnormalised pdfs. (defaults to TRUE)

Examples

# \donttest{
# 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")
predPrior <- predictSLGP_newNode(SLGPmodel=modelPrior,
                                 newNodes = dfGrid)
# }