SLSEdesign

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

\newcommand{\cv}{\operatorname{cv}}

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  cache = TRUE
)
original <- options(digits = 3)

Installation

# required dependencies
require(SLSEdesign)
require(CVXR)

Specify the input for the program

1. N: Number of design points

2. S: The design space

3. tt: The level of skewness

4. $\theta$: The parameter vector

5. FUN: The function for calculating the derivatives of the given model

N <- 21
S <- c(-1, 1)
tt <- 0
theta <- rep(1, 4)

poly3 <- function(xi,theta){
    matrix(c(1, xi, xi^2, xi^3), ncol = 1)
}

u <- seq(from = S[1], to = S[2], length.out = N)

res <- Aopt(N = N, u = u, tt = tt, FUN = poly3, 
            theta = theta)

Manage the outputs

Showing the optimal design and the support points

res$design

Or we can plot them

plot_weight(res$design)

Plot the directional derivative to use the equivalence theorem for 3rd order polynomial models

D-optimal design

poly3 <- function(xi,theta){
    matrix(c(1, xi, xi^2, xi^3), ncol = 1)
}
design <- data.frame(location = c(-1, -0.447, 0.447, 1),
 weight = rep(0.25, 4))
u = seq(-1, 1, length.out = 201)
plot_direction_Dopt(u, design, tt=0, FUN = poly3,
  theta = rep(0, 4))

A-optimal design

poly3 <- function(xi, theta){
  matrix(c(1, xi, xi^2, xi^3), ncol = 1)
}
design <- data.frame(location = c(-1, -0.464, 0.464, 1),
                    weight = c(0.151, 0.349, 0.349, 0.151))
u = seq(-1, 1, length.out = 201)
plot_direction_Aopt(u, design, tt=0, FUN = poly3, theta = rep(0,4))

options(original) # reset to old settings

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