{"title":"使用模板模型生成器的混合效果转换模型","authors":"Bálint Tamási, T. Hothorn","doi":"10.32614/rj-2021-075","DOIUrl":null,"url":null,"abstract":"Linear transformation models constitute a general family of parametric regression models for discrete and continuous responses. To accommodate correlated responses, the model is extended by incorporating mixed effects. This article presents the R package tramME , which builds on existing implementations of transformation models ( mlt and tram packages) as well as Laplace approximation and automatic differentiation (using the TMB package), to calculate estimates and perform likelihood inference in mixed-effects transformation models. The resulting framework can be readily applied to a wide range of regression problems with grouped data structures.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"21 1","pages":"306"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"tramME: Mixed-Effects Transformation Models Using Template Model Builder\",\"authors\":\"Bálint Tamási, T. Hothorn\",\"doi\":\"10.32614/rj-2021-075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Linear transformation models constitute a general family of parametric regression models for discrete and continuous responses. To accommodate correlated responses, the model is extended by incorporating mixed effects. This article presents the R package tramME , which builds on existing implementations of transformation models ( mlt and tram packages) as well as Laplace approximation and automatic differentiation (using the TMB package), to calculate estimates and perform likelihood inference in mixed-effects transformation models. The resulting framework can be readily applied to a wide range of regression problems with grouped data structures.\",\"PeriodicalId\":20974,\"journal\":{\"name\":\"R J.\",\"volume\":\"21 1\",\"pages\":\"306\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"R J.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32614/rj-2021-075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"R J.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32614/rj-2021-075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
tramME: Mixed-Effects Transformation Models Using Template Model Builder
Linear transformation models constitute a general family of parametric regression models for discrete and continuous responses. To accommodate correlated responses, the model is extended by incorporating mixed effects. This article presents the R package tramME , which builds on existing implementations of transformation models ( mlt and tram packages) as well as Laplace approximation and automatic differentiation (using the TMB package), to calculate estimates and perform likelihood inference in mixed-effects transformation models. The resulting framework can be readily applied to a wide range of regression problems with grouped data structures.