print {BKP} | R Documentation |
Print Summary of a Fitted BKP or DKP Model
Description
Displays a concise summary of a fitted BKP or DKP model. The output includes key characteristics such as sample size, input dimensionality, kernel type, loss function, optimized kernel hyperparameters, and minimum loss.
Usage
## S3 method for class 'BKP'
print(x, ...)
## S3 method for class 'DKP'
print(x, ...)
Arguments
x |
An object of class |
... |
Additional arguments passed to the generic |
Value
Invisibly returns the input object (of class "BKP"
or
"DKP"
). The function is called for its side effect of printing a
summary to the console.
See Also
fit.BKP
, fit.DKP
,
summary.BKP
, summary.DKP
.
Examples
# ============================================================== #
# ========================= BKP Examples ======================= #
# ============================================================== #
#-------------------------- 1D Example ---------------------------
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
model1 <- fit.BKP(X, y, m, Xbounds=Xbounds)
print(model1)
#-------------------------- 2D Example ---------------------------
set.seed(123)
# Define 2D latent function and probability transformation
true_pi_fun <- function(X) {
if(is.null(nrow(X))) X <- matrix(X, nrow=1)
m <- 8.6928
s <- 2.4269
x1 <- 4*X[,1]- 2
x2 <- 4*X[,2]- 2
a <- 1 + (x1 + x2 + 1)^2 *
(19- 14*x1 + 3*x1^2- 14*x2 + 6*x1*x2 + 3*x2^2)
b <- 30 + (2*x1- 3*x2)^2 *
(18- 32*x1 + 12*x1^2 + 48*x2- 36*x1*x2 + 27*x2^2)
f <- log(a*b)
f <- (f- m)/s
return(pnorm(f)) # Transform to probability
}
n <- 100
Xbounds <- matrix(c(0, 0, 1, 1), nrow = 2)
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
model2 <- fit.BKP(X, y, m, Xbounds=Xbounds)
print(model2)
# ============================================================== #
# ========================= DKP Examples ======================= #
# ============================================================== #
#-------------------------- 1D Example ---------------------------
set.seed(123)
# Define true class probability function (3-class)
true_pi_fun <- function(X) {
p <- (1 + exp(-X^2) * cos(10 * (1 - exp(-X)) / (1 + exp(-X)))) / 2
return(matrix(c(p/2, p/2, 1 - p), nrow = length(p)))
}
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)
# Generate multinomial responses
Y <- t(sapply(1:n, function(i) rmultinom(1, size = m[i], prob = true_pi[i, ])))
# Fit DKP model
model1 <- fit.DKP(X, Y, Xbounds = Xbounds)
print(model1)
#-------------------------- 2D Example ---------------------------
set.seed(123)
# Define latent function and transform to 3-class probabilities
true_pi_fun <- function(X) {
if (is.null(nrow(X))) X <- matrix(X, nrow = 1)
m <- 8.6928; s <- 2.4269
x1 <- 4 * X[,1] - 2
x2 <- 4 * X[,2] - 2
a <- 1 + (x1 + x2 + 1)^2 *
(19 - 14*x1 + 3*x1^2 - 14*x2 + 6*x1*x2 + 3*x2^2)
b <- 30 + (2*x1 - 3*x2)^2 *
(18 - 32*x1 + 12*x1^2 + 48*x2 - 36*x1*x2 + 27*x2^2)
f <- (log(a * b) - m) / s
p <- pnorm(f)
return(matrix(c(p/2, p/2, 1 - p), nrow = length(p)))
}
n <- 100
Xbounds <- matrix(c(0, 0, 1, 1), nrow = 2)
X <- tgp::lhs(n = n, rect = Xbounds)
true_pi <- true_pi_fun(X)
m <- sample(100, 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
model2 <- fit.DKP(X, Y, Xbounds = Xbounds)
print(model2)
[Package BKP version 0.1.0 Index]