Calculate the c-optimal design under the SLSE with the given combination of the parameters

Usage

copt(N, u, tt, FUN, theta, num_iter = 1000, cVec)

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 c-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.
cVec: c vector used to determine the combination of the parameters

Value

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

Details

This function calculates the c-optimal design and the loss function under the c-optimality. The loss function under c-optimality is defined as the log determinant of the inverse of the Fisher information matrix.

Examples

poly3 <- function(xi, theta){
  matrix(c(1, xi, xi^2, xi^3), ncol = 1)
}
Npt <- 101
my_design <- copt(N = Npt, u = seq(-1, +1, length.out = Npt),
   tt = 0, FUN = poly3, theta = rep(0,4), num_iter = 2000,
   cVec = c(0,1,1,1))
round(my_design$design, 3)
#>     location weight
#> 51         0    0.5
#> 101        1    0.5
my_design$val
#> [1] 3.999952