Aopt {SLSEdesign}

Calculate the A-optimal design under the second-order Least squares estimator

Description

Calculate the A-optimal design under the second-order Least squares estimator

Usage

Aopt(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 between 0 to 1 (inclusive). When tt=0, it is equivalent to compute the A-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 A-optimal design and the loss function under the A-optimality. The loss function under A-optimality is defined as the trace 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 <- Aopt(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