# Aopt: Calculate the A-optimal design under the second-order Least... In SLSEdesign: Optimal Regression Design under the Second-Order Least Squares Estimator

 Aopt R Documentation

## 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
``````

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