Keywords: Optimal design, Nonlinear models, Optimization, Evolutionary algorithm
Webpages:
https://cran.r-project.org/web/packages/ICAOD The
ICAOD package applies a novel multi-heuristic algorithm called imperialist competitive algorithm (ICA) to find different types of optimal designs for nonlinear models (Masoudi et al., in press). The setup assumes that we have a general parametric regression model and a design criterion formulated as a convex function of the Fisher information matrix. The package constructs locally D-optimal, minimax D-optimal, standardized maximin D-optimal and optimum-on-the-average designs for a class of nonlinear models, including multiple-objective optimal designs for the 4-parameter Hill model commonly used in dose response studies and other applied fields. Several useful functions are also provided in the package, namely a function to check optimality of the generated design using an equivalence theorem followed by a graphic plot of the sensitivity function for visual appreciation. Another function is to compute the efficiency lower bound of the generated design if the algorithm is terminated prematurely.
References Masoudi E., Holling H., Wong W.K. (in press) Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs,
Computational Statistics & Data Analysis.
