binGroup:用于组测试的软件包

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2010-12-01 DOI:10.32614/RJ-2010-016
C. Bilder, Boan Zhang, F. Schaarschmidt, J. Tebbs
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引用次数: 36

摘要

当某种疾病或某些其他二元特征的流行率很小时,通常使用群体检测(也称为合并检测)来估计流行率和/或确定个体为阳性或阴性。我们已经开发了binGroup包,作为第一个设计用于解决组测试中的评估问题的包。我们提出了估算同质人群总体患病率的函数。此外,对于这种设置,我们有一些函数来帮助选择非常重要的组大小。当个体来自异质群体时,我们的群体检验回归函数可用于仅使用群体观察值来估计个体疾病阳性的概率。我们用多载体转移设计实验和人类传染病流行研究的数据来说明我们的函数。
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binGroup: A Package for Group Testing
When the prevalence of a disease or of some other binary characteristic is small, group testing (also known as pooled testing) is frequently used to estimate the prevalence and/or to identify individuals as positive or negative. We have developed the binGroup package as the first package designed to address the estimation problem in group testing. We present functions to estimate an overall prevalence for a homogeneous population. Also, for this setting, we have functions to aid in the very important choice of the group size. When individuals come from a heterogeneous population, our group testing regression functions can be used to estimate an individual probability of disease positivity by using the group observations only. We illustrate our functions with data from a multiple vector transfer design experiment and a human infectious disease prevalence study.
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
期刊最新文献
binGroup2: Statistical Tools for Infection Identification via Group Testing. glmmPen: High Dimensional Penalized Generalized Linear Mixed Models. Three-Way Correspondence Analysis in R nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting A Workflow for Estimating and Visualising Excess Mortality During the COVID-19 Pandemic
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