{"title":"稳健贝叶斯增长曲线建模:使用JAGS的教程","authors":"Ruoxuan Li","doi":"10.35566/jbds/v3n2/li","DOIUrl":null,"url":null,"abstract":"Latent growth curve models (LGCM) are widely used in longitudinal data analysis, and robust methods can be used to model error distributions for non-normal data. This tutorial introduces how to modellinear, non-linear, and quadratic growth curve models under the Bayesian framework and uses examples to illustrate how to model errors using t, exponential power, and skew-normal distributions. The code of JAGS models is provided and implemented by the R package runjags. Model diagnostics and comparisons are briefly discussed.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust Bayesian growth curve modeling: A tutorial using JAGS\",\"authors\":\"Ruoxuan Li\",\"doi\":\"10.35566/jbds/v3n2/li\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Latent growth curve models (LGCM) are widely used in longitudinal data analysis, and robust methods can be used to model error distributions for non-normal data. This tutorial introduces how to modellinear, non-linear, and quadratic growth curve models under the Bayesian framework and uses examples to illustrate how to model errors using t, exponential power, and skew-normal distributions. The code of JAGS models is provided and implemented by the R package runjags. Model diagnostics and comparisons are briefly discussed.\",\"PeriodicalId\":93575,\"journal\":{\"name\":\"Journal of behavioral data science\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of behavioral data science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35566/jbds/v3n2/li\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of behavioral data science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35566/jbds/v3n2/li","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust Bayesian growth curve modeling: A tutorial using JAGS
Latent growth curve models (LGCM) are widely used in longitudinal data analysis, and robust methods can be used to model error distributions for non-normal data. This tutorial introduces how to modellinear, non-linear, and quadratic growth curve models under the Bayesian framework and uses examples to illustrate how to model errors using t, exponential power, and skew-normal distributions. The code of JAGS models is provided and implemented by the R package runjags. Model diagnostics and comparisons are briefly discussed.