Generating spatially constrained null models for irregularly spaced data using Moran spectral randomization methods

By H. Wagner, Stéphane Dray. 2015

114 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/568810c343fe6c045dffacab96351668b2937886

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Citations in the corpus

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets (2019, methodology): “To this end, we provide two methods: spin permutations 46 and Moran spectral randomization (MSR) 56,57 .”
Disentangling good from bad practices in the selection of spatial or phylogenetic eigenvectors (2018, methodology): “2008), and 2) introduce them in a VP in which the fractions are tested and estimated on the basis of an R² corrected by a null model constrained with Moran spectral randomisation (Wagner and Dray 2015) to maintain the spatial structures of the data (Clappe et al. pers. comm.).”
Testing the Mantel statistic with a spatially‐constrained permutation procedure (2019, methodology): “To address the issue of spatial autocorrelation in testing the Mantel statistic, we used the MSR (Wagner & Dray, 2015) instead of standard permutation procedure.”
Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co‐occurrence (2018, methodology): “(2014), but other methods to account spatial patterns that are more complex than the ones solely captured by the geographical coordinates could also be applied to refine these results (Wagner and Dray 2015).”
Comparing spatial null models for brain maps (2021, unknown): “…in ﬁelds like geostatistics and ecology ( Cliﬀ and ; Legendre, 1993 ), and there have been signiﬁcant developments in these ﬁelds to account for and overcome its inﬂuence ( Cressie, 2015; Deblauwe et al., 2012; Dray, 2011; Dutilleul et al., 1993; Fortin and Jacquez, 2000; Wagner and Dray, 2015 ).”