Optimizing the choice of a spatial weighting matrix in eigenvector-based methods. Bauman, D., Drouet, T., Fortin, M., J., & Dray, S. Ecology, 99(10):2159-2166, 2018. doi abstract bibtex Abstract Eigenvector-mapping methods such as Moran's eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the most-widely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.
@article{
title = {Optimizing the choice of a spatial weighting matrix in eigenvector-based methods},
type = {article},
year = {2018},
keywords = {Moran's eigenvector maps (MEM),community ecology,community simulation,connection scheme,inference of ecological processes from spatial pat,multiscale spatial patterns,optimization,principal coordinates of neighbor matrices (PCNM),spatial autocorrelation,spatial eigenvector mapping (SEVM),spatial weighting matrix,type I error rate inflation},
pages = {2159-2166},
volume = {99},
id = {0e6eeb9b-c61c-3d11-bee6-8ce92dc58db8},
created = {2018-11-19T09:13:27.174Z},
file_attached = {true},
profile_id = {976aa121-3316-304c-8340-7ca54d70abe6},
last_modified = {2020-08-07T12:49:26.001Z},
read = {false},
starred = {false},
authored = {true},
confirmed = {true},
hidden = {false},
citation_key = {Bauman2018a},
private_publication = {false},
abstract = {Abstract Eigenvector-mapping methods such as Moran's eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the most-widely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.},
bibtype = {article},
author = {Bauman, David and Drouet, Thomas and Fortin, Marie Josée and Dray, Stéphane},
doi = {10.1002/ecy.2469},
journal = {Ecology},
number = {10}
}
Downloads: 0
{"_id":"v9JuWQi3Z7PYwwJmb","bibbaseid":"bauman-drouet-fortin-dray-optimizingthechoiceofaspatialweightingmatrixineigenvectorbasedmethods-2018","author_short":["Bauman, D.","Drouet, T.","Fortin, M., J.","Dray, S."],"bibdata":{"title":"Optimizing the choice of a spatial weighting matrix in eigenvector-based methods","type":"article","year":"2018","keywords":"Moran's eigenvector maps (MEM),community ecology,community simulation,connection scheme,inference of ecological processes from spatial pat,multiscale spatial patterns,optimization,principal coordinates of neighbor matrices (PCNM),spatial autocorrelation,spatial eigenvector mapping (SEVM),spatial weighting matrix,type I error rate inflation","pages":"2159-2166","volume":"99","id":"0e6eeb9b-c61c-3d11-bee6-8ce92dc58db8","created":"2018-11-19T09:13:27.174Z","file_attached":"true","profile_id":"976aa121-3316-304c-8340-7ca54d70abe6","last_modified":"2020-08-07T12:49:26.001Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"citation_key":"Bauman2018a","private_publication":false,"abstract":"Abstract Eigenvector-mapping methods such as Moran's eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the most-widely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.","bibtype":"article","author":"Bauman, David and Drouet, Thomas and Fortin, Marie Josée and Dray, Stéphane","doi":"10.1002/ecy.2469","journal":"Ecology","number":"10","bibtex":"@article{\n title = {Optimizing the choice of a spatial weighting matrix in eigenvector-based methods},\n type = {article},\n year = {2018},\n keywords = {Moran's eigenvector maps (MEM),community ecology,community simulation,connection scheme,inference of ecological processes from spatial pat,multiscale spatial patterns,optimization,principal coordinates of neighbor matrices (PCNM),spatial autocorrelation,spatial eigenvector mapping (SEVM),spatial weighting matrix,type I error rate inflation},\n pages = {2159-2166},\n volume = {99},\n id = {0e6eeb9b-c61c-3d11-bee6-8ce92dc58db8},\n created = {2018-11-19T09:13:27.174Z},\n file_attached = {true},\n profile_id = {976aa121-3316-304c-8340-7ca54d70abe6},\n last_modified = {2020-08-07T12:49:26.001Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Bauman2018a},\n private_publication = {false},\n abstract = {Abstract Eigenvector-mapping methods such as Moran's eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the most-widely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.},\n bibtype = {article},\n author = {Bauman, David and Drouet, Thomas and Fortin, Marie Josée and Dray, Stéphane},\n doi = {10.1002/ecy.2469},\n journal = {Ecology},\n number = {10}\n}","author_short":["Bauman, D.","Drouet, T.","Fortin, M., J.","Dray, S."],"biburl":"https://bibbase.org/service/mendeley/2845861","bibbaseid":"bauman-drouet-fortin-dray-optimizingthechoiceofaspatialweightingmatrixineigenvectorbasedmethods-2018","role":"author","urls":{},"keyword":["Moran's eigenvector maps (MEM)","community ecology","community simulation","connection scheme","inference of ecological processes from spatial pat","multiscale spatial patterns","optimization","principal coordinates of neighbor matrices (PCNM)","spatial autocorrelation","spatial eigenvector mapping (SEVM)","spatial weighting matrix","type I error rate inflation"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/2845861","dataSources":["2252seNhipfTmjEBQ"],"keywords":["moran's eigenvector maps (mem)","community ecology","community simulation","connection scheme","inference of ecological processes from spatial pat","multiscale spatial patterns","optimization","principal coordinates of neighbor matrices (pcnm)","spatial autocorrelation","spatial eigenvector mapping (sevm)","spatial weighting matrix","type i error rate inflation"],"search_terms":["optimizing","choice","spatial","weighting","matrix","eigenvector","based","methods","bauman","drouet","fortin","dray"],"title":"Optimizing the choice of a spatial weighting matrix in eigenvector-based methods","year":2018}