{"title":"seawaveq -一个R软件包,提供了一个模型和实用程序,用于分析具有季节性波(海浪)的溪流中的化学浓度趋势,并调整溪流流量(Q)和其他辅助变量,版本2.0.0","authors":"K. Ryberg, Benjamin C. York","doi":"10.3133/OFR20131255","DOIUrl":null,"url":null,"abstract":"This R package, seawaveQ, is designed for fitting a parametric regression model for assessing variability and trends in pesticide concentration in streams and was developed by Vecchia and others (2008), and subsequently refined and referred to as the“SEAWAVE-Q”model in several trend analyses (Ryberg and others, 2010; Sullivan and others, 2009; Vecchia and others, 2009). In these publications, “SEAWAVE-Q”stands for seasonal wave (SEAWAVE) with adjustment for streamflow (Q). The model was developed to“handle a number of difficulties often found in pesticide data, such as strong seasonality in response to use patterns, high numbers of concentrations below laboratory","PeriodicalId":142152,"journal":{"name":"Open-File Report","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"seawaveQ—An R package providing a model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables, version 2.0.0\",\"authors\":\"K. Ryberg, Benjamin C. York\",\"doi\":\"10.3133/OFR20131255\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This R package, seawaveQ, is designed for fitting a parametric regression model for assessing variability and trends in pesticide concentration in streams and was developed by Vecchia and others (2008), and subsequently refined and referred to as the“SEAWAVE-Q”model in several trend analyses (Ryberg and others, 2010; Sullivan and others, 2009; Vecchia and others, 2009). In these publications, “SEAWAVE-Q”stands for seasonal wave (SEAWAVE) with adjustment for streamflow (Q). The model was developed to“handle a number of difficulties often found in pesticide data, such as strong seasonality in response to use patterns, high numbers of concentrations below laboratory\",\"PeriodicalId\":142152,\"journal\":{\"name\":\"Open-File Report\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Open-File Report\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3133/OFR20131255\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open-File Report","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3133/OFR20131255","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
seawaveQ—An R package providing a model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables, version 2.0.0
This R package, seawaveQ, is designed for fitting a parametric regression model for assessing variability and trends in pesticide concentration in streams and was developed by Vecchia and others (2008), and subsequently refined and referred to as the“SEAWAVE-Q”model in several trend analyses (Ryberg and others, 2010; Sullivan and others, 2009; Vecchia and others, 2009). In these publications, “SEAWAVE-Q”stands for seasonal wave (SEAWAVE) with adjustment for streamflow (Q). The model was developed to“handle a number of difficulties often found in pesticide data, such as strong seasonality in response to use patterns, high numbers of concentrations below laboratory