fdesigns: Bayesian Optimal Designs of Experiments for Functional Models in R

This paper describes the R package fdesigns that implements a methodology for identifying Bayesian optimal experimental designs for models whose factor settings are functions, known as profile factors. This type of experiments which involve factors that vary dynamically over time, presenting unique...

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Bibliographic Details
Published in:arXiv.org 2024-11
Main Authors: Michaelides, Damianos, Overstall, Antony, Woods, Dave
Format: Article
Language:English
Subjects:
B spline functions
Basis functions
Bayesian analysis
Design factors
Design optimization
Functionals
Polynomials
Online Access:Get full text
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Summary:This paper describes the R package fdesigns that implements a methodology for identifying Bayesian optimal experimental designs for models whose factor settings are functions, known as profile factors. This type of experiments which involve factors that vary dynamically over time, presenting unique challenges in both estimation and design due to the infinite-dimensional nature of functions. The package fdesigns implements a dimension reduction method leveraging basis functions of the B-spline basis system. The package fdesigns contains functions that effectively reduce the design problem to the optimisation of basis coefficients for functional linear functional generalised linear models, and it accommodates various options. Applications of the fdesigns package are demonstrated through a series of examples that showcase its capabilities in identifying optimal designs for functional linear and generalised linear models. The examples highlight how the package's functions can be used to efficiently design experiments involving both profile and scalar factors, including interactions and polynomial effects.
ISSN:2331-8422