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block CV : An r package for generating spatially or environmentally separated folds for k ‐fold cross‐validation of species distribution models
Author(s) -
Valavi Roozbeh,
Elith Jane,
LahozMonfort José J.,
GuilleraArroita Gurutzeta
Publication year - 2019
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13107
Subject(s) - toolbox , r package , computer science , spatial analysis , block (permutation group theory) , autocorrelation , environmental niche modelling , data mining , statistics , mathematics , ecology , biology , ecological niche , computational science , programming language , geometry , habitat
Abstract 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 block CV , 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 block CV 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.