calc_phi: Calculate the loss function of the A-, c- or D-optimal design

In SLSEdesign: Optimal Regression Design under the Second-Order Least Squares Estimator

Calculate the loss function of the A-, c- or D-optimal design

Description

Calculate the loss function of the A-, c- or D-optimal design

Usage

calc_phi(
  design,
  theta,
  FUN,
  tt,
  A,
  criterion = "D",
  cVec = rep(0, length(theta))
)

Arguments

design	The resulted design that contains the design points and the associated weights
theta	The parameter value of the model
FUN	The function to calculate the derivative of the given model.
tt	The level of skewness
A	The calculated covariance matrix
criterion	The criterion to be used for the design, either "D" for D-optimality or "A" for A-optimality. Default is "D".
cVec	c vector used to determine the combination of the parameters. This is only used in c-optimality

Details

This function calculates the loss function of the design problem under the A- or D-optimality. The loss functions under A-, or D-optimality are defined as the trace and log determinant of the inverse of the Fisher information matrix

Value

The loss of the model at each design points

Examples

my_design <- data.frame(location = c(0, 180), weight = c(1/2, 1/2))
theta <- c(0.05, 0.5)
peleg <- function(xi, theta){
   deno <- (theta[1] + xi * theta[2])^2
   rbind(-xi/deno, -xi^2/deno)
}
A <- matrix(c(1, 0, 0, 0, 0.2116, 1.3116, 0, 1.3116, 15.462521), byrow = TRUE, ncol = 3)
res <- calc_phi(my_design, theta, peleg, 0, A, criterion = "A")
res

SLSEdesign documentation built on June 8, 2025, 1:47 p.m.