calc_phiD: Calculate the loss function of the D-optimal design In SLSEdesign: Optimal Regression Design under the Second-Order Least Squares Estimator

 calc_phiD R Documentation

Calculate the loss function of the D-optimal design

Description

Calculate the loss function of the D-optimal design

Usage

``````calc_phiD(design, theta, FUN, tt, A)
``````

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

Details

This function calculates the loss function of the design problem under the D-optimality. The loss function under D-optimality is defined as the 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_phiA(my_design, theta, peleg, 0, A)
res
``````

SLSEdesign documentation built on June 22, 2024, 9:45 a.m.