Gonogo: An R Implementation of Test Methods to Perform, Analyze and Simulate Sensitivity Experiments
Abstract
This work provides documentation for a suite of R functions contained in gonogo.R. The functions provide sensitivity testing practitioners and researchers with an ability to conduct, analyze and simulate various sensitivity experiments involving binary responses and a single stimulus level (e.g., drug dosage, drop height, velocity, etc.). Included are the modern Neyer and 3pod adaptive procedures, as well as the Bruceton and Langlie. The latter two benchmark procedures are capable of being performed according to generalized up-down transformed-response rules. Each procedure is designated phase-one of a three-phase experiment. The goal of phase-one is to achieve overlapping data. The two additional (and optional) refinement phases utilize the D-optimal criteria and the Robbins-Monro-Joseph procedure. The goals of the two refinement phases are to situate testing in the vicinity of the median and tails of the latent response distribution, respectively.
Cite
@article{arxiv.2011.11177,
title = {Gonogo: An R Implementation of Test Methods to Perform, Analyze and Simulate Sensitivity Experiments},
author = {Paul A. Roediger},
journal= {arXiv preprint arXiv:2011.11177},
year = {2020}
}
Comments
This documentation is 58 pages in length and contains 31 figures, 40 tables and 2 flow diagrams. The subject of much of the paper, the gonogo.R file, contains 118 functions plus 2 constants and is available online