blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models
blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models
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DOI：
10.1111/2041-210x.13107
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发表时间：
2019-02-01
影响因子：
6.6
通讯作者：
Guillera-Arroita, Gurutzeta
中科院分区：
文献类型：
--
作者：
Valavi, Roozbeh;Elith, Jane;Guillera-Arroita, Gurutzeta
当应用于结构化数据时，传统的随机交叉验证技术可能会导致低估预测误差，并可能导致不适当的模型选择。我们提出了r包blockCV，这是一个用于交叉验证物种分布模型的新工具箱。虽然它是在考虑物种分布建模的情况下开发的，但它可以用于任何空间建模。包装可以产生空间上或环境上分离的折叠。它包括测量候选协变量的空间自相关范围的工具，为用户提供了对这些数据的空间结构的见解。它还提供了交互式图形功能，用于创建空间块和探索数据折叠。包blockCV使建模人员能够更容易地实现一系列评估方法。它将帮助建模社区更多地了解评估方法对我们理解物种分布模型预测性能的影响。
When applied to structured data, conventional random cross-validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection. We present the r package blockCV, a new toolbox for cross-validation of species distribution modelling. Although it has been developed with species distribution modelling in mind, it can be used for any spatial modelling. The package can generate spatially or environmentally separated folds. It includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data. It also offers interactive graphical capabilities for creating spatial blocks and exploring data folds. Package blockCV enables modellers to more easily implement a range of evaluation approaches. It will help the modelling community learn more about the impacts of evaluation approaches on our understanding of predictive performance of species distribution models.