{"title":"从配对比较数据估计选择模型参数的Matlab函数。","authors":"Florian Wickelmaier, Christian Schmid","doi":"10.3758/bf03195547","DOIUrl":null,"url":null,"abstract":"<p><p>Tversky (1972) has proposed a family of models for paired-comparison data that generalize the Bradley-Terry-Luce (BTL) model and can, therefore, apply to a diversity of situations in which the BTL model is doomed to fail. In this article, we present a Matlab function that makes it easy to specify any of these general models (EBA, Pretree, or BTL) and to estimate their parameters. The program eliminates the time-consuming task of constructing the likelihood function by hand for every single model. The usage of the program is illustrated by several examples. Features of the algorithm are outlined. The purpose of this article is to facilitate the use of probabilistic choice models in the analysis of data resulting from paired comparisons.</p>","PeriodicalId":79800,"journal":{"name":"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc","volume":"36 1","pages":"29-40"},"PeriodicalIF":0.0000,"publicationDate":"2004-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3758/bf03195547","citationCount":"148","resultStr":"{\"title\":\"A Matlab function to estimate choice model parameters from paired-comparison data.\",\"authors\":\"Florian Wickelmaier, Christian Schmid\",\"doi\":\"10.3758/bf03195547\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Tversky (1972) has proposed a family of models for paired-comparison data that generalize the Bradley-Terry-Luce (BTL) model and can, therefore, apply to a diversity of situations in which the BTL model is doomed to fail. In this article, we present a Matlab function that makes it easy to specify any of these general models (EBA, Pretree, or BTL) and to estimate their parameters. The program eliminates the time-consuming task of constructing the likelihood function by hand for every single model. The usage of the program is illustrated by several examples. Features of the algorithm are outlined. The purpose of this article is to facilitate the use of probabilistic choice models in the analysis of data resulting from paired comparisons.</p>\",\"PeriodicalId\":79800,\"journal\":{\"name\":\"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc\",\"volume\":\"36 1\",\"pages\":\"29-40\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3758/bf03195547\",\"citationCount\":\"148\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3758/bf03195547\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3758/bf03195547","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Matlab function to estimate choice model parameters from paired-comparison data.
Tversky (1972) has proposed a family of models for paired-comparison data that generalize the Bradley-Terry-Luce (BTL) model and can, therefore, apply to a diversity of situations in which the BTL model is doomed to fail. In this article, we present a Matlab function that makes it easy to specify any of these general models (EBA, Pretree, or BTL) and to estimate their parameters. The program eliminates the time-consuming task of constructing the likelihood function by hand for every single model. The usage of the program is illustrated by several examples. Features of the algorithm are outlined. The purpose of this article is to facilitate the use of probabilistic choice models in the analysis of data resulting from paired comparisons.