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 = 3,
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
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)
#> Fitting AddiVortes model to input data...
#> Input dimensions: 10 observations, 5 covariates
#> Model configuration: 5 tessellations, 50 total iterations (10 burn-in)
#>
#> Phase 1: Burn-in sampling (10 iterations)
#>
|
| | 0%
|
|===== | 10%
|
|========== | 20%
|
|=============== | 30%
|
|==================== | 40%
|
|========================= | 50%
|
|============================== | 60%
|
|=================================== | 70%
|
|======================================== | 80%
|
|============================================= | 90%
|
|==================================================| 100%
#>
#> Phase 2: Posterior sampling (40 iterations)
#>
|
| | 0%
|
|== | 5%
|
|==== | 8%
|
|===== | 10%
|
|====== | 12%
|
|======== | 15%
|
|========= | 18%
|
|========== | 20%
|
|=========== | 22%
|
|============ | 25%
|
|============== | 28%
|
|=============== | 30%
|
|================ | 32%
|
|================== | 35%
|
|=================== | 38%
|
|==================== | 40%
|
|===================== | 42%
|
|====================== | 45%
|
|======================== | 48%
|
|========================= | 50%
|
|========================== | 52%
|
|============================ | 55%
|
|============================= | 58%
|
|============================== | 60%
|
|=============================== | 62%
|
|================================ | 65%
|
|================================== | 68%
|
|=================================== | 70%
|
|==================================== | 72%
|
|====================================== | 75%
|
|======================================= | 78%
|
|======================================== | 80%
|
|========================================= | 82%
|
|========================================== | 85%
|
|============================================ | 88%
|
|============================================= | 90%
|
|============================================== | 92%
|
|================================================ | 95%
|
|================================================= | 98%
|
|==================================================| 100%
#>
#> MCMC sampling completed.
#>
# }