BKP (version 0.2.3)

parameter: Extract Model Parameters from a Fitted BKP or DKP Model

Description

Retrieve the key model parameters from a fitted BKP or DKP object. For a BKP model, this typically includes the optimized kernel hyperparameters and posterior Beta parameters. For a DKP model, this includes the kernel hyperparameters and posterior Dirichlet parameters.

Usage

parameter(object, ...)
# S3 method for BKP
parameter(object, ...)
# S3 method for DKP
parameter(object, ...)

Value

A named list containing:

• theta: Estimated kernel hyperparameters.

• alpha_n: Posterior Dirichlet/Beta \(\alpha\) parameters.

• beta_n: (BKP only) Posterior Beta \(\beta\) parameters.

Arguments

object

An object of class BKP or DKP, typically the result of a call to fit_BKP or fit_DKP.

...

Additional arguments (currently unused).

References

Zhao J, Qing K, Xu J (2025). BKP: An R Package for Beta Kernel Process Modeling. arXiv. https://doi.org/10.48550/arXiv.2508.10447.

See Also

fit_BKP for fitting BKP models, fit_DKP for fitting DKP models.

Examples

# -------------------------- BKP ---------------------------
set.seed(123)

# Define true success probability function
true_pi_fun <- function(x) {
  (1 + exp(-x^2) * cos(10 * (1 - exp(-x)) / (1 + exp(-x)))) / 2
}

n <- 30
Xbounds <- matrix(c(-2, 2), nrow = 1)
X <- tgp::lhs(n = n, rect = Xbounds)
true_pi <- true_pi_fun(X)
m <- sample(100, n, replace = TRUE)
y <- rbinom(n, size = m, prob = true_pi)

# Fit BKP model
model <- fit_BKP(X, y, m, Xbounds = Xbounds)

# Extract posterior and kernel parameters
parameter(model)

# -------------------------- DKP ---------------------------
#' set.seed(123)

# Define true class probability function (3-class)
true_pi_fun <- function(X) {
  p1 <- 1/(1+exp(-3*X))
  p2 <- (1 + exp(-X^2) * cos(10 * (1 - exp(-X)) / (1 + exp(-X)))) / 2
  return(matrix(c(p1/2, p2/2, 1 - (p1+p2)/2), nrow = length(p1)))
}

n <- 30
Xbounds <- matrix(c(-2, 2), nrow = 1)
X <- tgp::lhs(n = n, rect = Xbounds)
true_pi <- true_pi_fun(X)
m <- sample(150, n, replace = TRUE)

# Generate multinomial responses
Y <- t(sapply(1:n, function(i) rmultinom(1, size = m[i], prob = true_pi[i, ])))

# Fit DKP model
model <- fit_DKP(X, Y, Xbounds = Xbounds)

# Extract posterior quantiles
parameter(model)