AddiVortes

The AddiVortes function is a Bayesian nonparametric regression model that uses a tessellation to model the relationship between the covariates and the output values. The model uses a backfitting algorithm to sample from the posterior distribution of the output values for each tessellation. The function returns the RMSE value for the test samples.

Usage

AddiVortes(
  y,
  x,
  m = 200,
  totalMCMCIter = 1200,
  mcmcBurnIn = 200,
  nu = 6,
  q = 0.85,
  k = 3,
  sd = 0.8,
  Omega = min(3, ncol(x)),
  LambdaRate = 25,
  IntialSigma = "Linear",
  thinning = 1,
  showProgress = TRUE
)

Arguments

y

A vector of the output values.

x

A matrix of the covariates.

m

The number of tessellations.

totalMCMCIter

The number of iterations.

mcmcBurnIn

The number of burn in iterations.

nu

The degrees of freedom.

q

The quantile.

k

The number of centres.

sd

The standard deviation.

Omega

Omega/(number of covariates) is the prior probability of adding a dimension.

LambdaRate

The rate of the Poisson distribution for the number of centres.

IntialSigma

The method used to calculate the initial variance.

thinning

The thinning rate.

showProgress

Logical; if TRUE (default), progress bars and messages are shown during fitting.

Value

An AddiVortesFit object containing the posterior samples of the tessellations, dimensions and predictions.

Examples

# \donttest{
# Simple example with simulated data
set.seed(123)
x <- matrix(rnorm(50), 10, 5)
y <- rnorm(10)
# Fit model with reduced iterations for quick example
fit <- AddiVortes(y, x, m = 5, totalMCMCIter = 50, mcmcBurnIn = 10)
# }