spsurvey:R 中的空间抽样设计与分析。

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Michael Dumelle, Tom Kincaid, Anthony R Olsen, Marc Weber
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引用次数: 0

摘要

spsurvey 提供广义随机分层(GRTS)算法,通过 grts() 函数选择空间平衡样本。grts() 函数可灵活适应多种抽样设计特征,包括分层、不同的包含概率、遗留(或历史)站点、站点之间的最小距离以及两个替换站点选项。spsurvey 还提供了一套数据分析选项,包括分类变量分析 (cat_analysis())、连续变量分析 cont_analysis())、相对风险分析 (relrisk_analysis())、可归因风险分析 (attrisk_analysis())、风险差异分析 (diffrisk_analysis())、变化分析 (change_analysis()) 和趋势分析 (trend_analysis())。在本手稿中,我们首先介绍了 GRTS 算法和分析方法的背景,然后展示了如何在 spsurvey 中实现它们。我们发现,与忽略空间信息的简单随机抽样相比,空间平衡 GRTS 算法能得到更精确的参数估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
spsurvey: Spatial Sampling Design and Analysis in R.

spsurvey is an R package for design-based statistical inference, with a focus on spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) algorithm to select spatially balanced samples via the grts() function. The grts() function flexibly accommodates several sampling design features, including stratification, varying inclusion probabilities, legacy (or historical) sites, minimum distances between sites, and two options for replacement sites. spsurvey also provides a suite of data analysis options, including categorical variable analysis (cat_analysis()), continuous variable analysis cont_analysis()), relative risk analysis (relrisk_analysis()), attributable risk analysis (attrisk_analysis()), difference in risk analysis (diffrisk_analysis()), change analysis (change_analysis()), and trend analysis (trend_analysis()). In this manuscript, we first provide background for the GRTS algorithm and the analysis approaches and then show how to implement them in spsurvey. We find that the spatially balanced GRTS algorithm yields more precise parameter estimates than simple random sampling, which ignores spatial information.

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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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