Testing environmental and genetic effects in the presence of spatial autocorrelation

By François Rousset, Jean-Baptiste Ferdy. 2014

369 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Genetics

View Paper: https://www.semanticscholar.org/paper/30291594546cc25b64c645c2091c4a70b83a860e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. T. Datry
2. Sylvie Clappe
3. J. Crabot
4. Stéphane Dray
5. X. Harrison
6. Pierre Legendre

In Genetics

1. T. Datry
2. Sylvie Clappe
3. Stéphane Dray
4. X. Harrison
5. J. Crabot

Citations in the corpus

• Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, neutral): “81 and 82).”
• A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution (2015, methodology): “The Bayesian framework is extremely flexible, meaning models following these equations can be easily specified, even though few frequentist mixed model packages in R permit the fitting of Beta-Binomial models (but see spaMM, Rousset & Ferdy, 2014; glmmADMB, Fournier et al., 2012).”
• Testing the Mantel statistic with a spatially‐constrained permutation procedure (2019, background): “In genetics, for instance, Guillot, Vitalis, le Rouzic, and Gautier (2014) developed a spatially explicit model that directly consider autocorrelation and Rousset and Ferdy (2014) presented fitting procedures for spatial GLMM providing correct estimate of correlation parameters.”