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A comparison of models for the quantal response on tumor incidence data in mixture experiments
Journal of the Korean Data & Information Science Society 2017;28:1021-6
Published online September 30, 2017
© 2017 Korean Data & Information Science Society.

Jung Il Kim1

Department of Information Statistics, Kangwon National University
Correspondence to: Jung Il Kim
Professor, Department of Information Statistics, Kangwon National University, Gangwon-do 200-701, Korea. E-mail: jikim@kangwon.ac.kr
Received August 21, 2017; Revised September 14, 2017; Accepted September 19, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Mixture experiments are commonly encountered in many fields including food, chemical and pharmaceutical industries. In mixture experiments, measured response depends on the proportions of the components present in the mixture and not on the amount of the mixture. Statistical analysis of the data from mixture experiments has mainly focused on a continuous response variable. In the example of quantal response data in mixture experiments, however, the tumor incidence data have been analyzed in Chen et al. (1996) to study the effects of 3 dietary components on the expression of mammary gland tumor. In this paper, we compared the logistic regression models with linear predictors such as second degree Scheffe polynomial model, Becker model and Akay model in terms of classification accuracy.
Keywords : Classification accuracy, logistic regression, mixture experiments, quantal response, second degree Scheffe polynomial model