Dopt: Calculate the D-optimal design under the SLSE

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

Calculate the D-optimal design under the SLSE

Description

Calculate the D-optimal design under the SLSE

Usage

Dopt(N, u, tt, FUN, theta, num_iter = 1000)

Arguments

N	The number of sample points in the design space.
u	The discretized design space.
tt	The level of skewness. When tt=0, it is equivalent to compute the D-optimal design under the ordinary least squares estimator.
FUN	The function to calculate the derivative of the given model.
theta	The parameter value of the model.
num_iter	Maximum number of iteration.

Details

This function calculates the D-optimal design and the loss function 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

A list that contains 1. Value of the objective function at solution. 2. Status. 3. Optimal design

Examples

poly3 <- function(xi, theta){
  matrix(c(1, xi, xi^2, xi^3), ncol = 1)
}
Npt <- 101
my_design <- Dopt(N = Npt, u = seq(-1, +1, length.out = Npt),
   tt = 0, FUN = poly3, theta = rep(0,4), num_iter = 2000)
round(my_design$design, 3)
my_design$val

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