oed_for_slope_over_intercept {optDesignSlopeInt}

Create an optimal design for measuring the slope divided by the intercept

Description

Create an optimal design for measuring the slope divided by the intercept

Usage

oed_for_slope_over_intercept(
  n,
  xmin,
  xmax,
  theta0,
  f_hetero = NULL,
  MaxIter = 6000,
  MaxFunEvals = 6000,
  TolFun = 1e-06,
  NUM_RAND_STARTS = 50
)

Arguments

n

The number of experimental runs.

xmin

The minimum value of the independent variable.

xmax

The maximum value of the independent variable.

theta0

The guess of the true value of the slope / intercept.

f_hetero

Specification of heteroskedasticity: the h(x) which relates the value of the independent variable to the variance in the response around the line at that place or the proportional variance at that point. If NULL, homoskedasticity is assumed (this is the default behavior).

MaxIter

For the heteroskedastic design, a Nelder-Mead search is used (via the function fminbnd). This is the MaxIter value for the search. Default is 6000. Lower if n is high.

MaxFunEvals

For the heteroskedastic design, a Nelder-Mead search is used (via the function fminbnd). This is the MaxFunEvals value for the search. Default is 6000. Lower if n is high.

TolFun

For the heteroskedastic design, a Nelder-Mead search is used (via the function fminbnd). This is the TolFun value for the search. Default is 1e-6. Increase for faster execution.

NUM_RAND_STARTS

For the heteroskedastic design, a Nelder-Mead search is used (via the function fminbnd). The Nelder-Mead search must be given a starting location. Our implementation uses many starting locations. This parameter controls the number of additional random starting locations in the space [xmin, xmax]. Default is 50.

Value

An n-vector of x-values which specifies the optimal design

Author(s)

Adam Kapelner

Examples

xmin = 5 / 15
xmax = 19 / 1
n = 10
theta0 = 0.053
opt_homo_design = oed_for_slope_over_intercept(n, xmin, xmax, theta0)
table(opt_homo_design)