Fx_dose: Matrix of candidate regressors for a dose-response model

In OptimalDesign: A Toolbox for Computing Efficient Designs of Experiments

Matrix of candidate regressors for a dose-response model

Description

Creates the matrix of all candidate regressors for a linearization of a dose response model.

Usage

Fx_dose(dose.levels, theta0, dose.model="emax", echo=TRUE)

Arguments

dose.levels	the n-dimensional vector of admissible doses.
theta0	the 3-dimensional vector of values of the unknown parameter in which to linearize the model.
dose.model	the type of the dose-response model, possible values are "emax", "loglin", and "exp".
echo	Print the call of the function?

Details

For mathematical details, see the referenced paper.

Value

The n times 3 matrix of all candidate regressors of a dose-response model linearized in theta0.

Author(s)

Radoslav Harman, Lenka Filova

References

Dette H, Kiss C, Bevanda M, Bretz F (2010). Optimal designs for the EMAX, log-linear and exponential models. Biometrika, 97(2), 513-518.

See Also

Fx_cube, Fx_simplex, Fx_blocks, Fx_glm, Fx_survival

Examples

# The loglinear model for the doses 1:150
# Localized at the values of theta0=c(0, 0.0797, 1)
Fx <- Fx_dose(1:150, c(0, 0.0797, 1), dose.model="loglin")

# The locally D-optimal approximate design
w_a <- od_REX(Fx)$w.best
od_plot(Fx, w_a, 1:150)

# The locally D-optimal exact design of size 10
w_e <- od_KL(Fx, 10, t.max=3)$w.best
od_plot(Fx, w_e, 1:150)

OptimalDesign documentation built on June 8, 2025, 12:08 p.m.