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Spatial Regression Models for Field Trials: A Comparative Study and New Ideas
Naturally occurring variability within a study region harbors valuable information on relationships between biological variables. Yet, spatial patterns within these study areas, e.g., in field trials, violate the assumption of independence of observations, setting particular challenges in terms of h...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006620/ https://www.ncbi.nlm.nih.gov/pubmed/35432426 http://dx.doi.org/10.3389/fpls.2022.858711 |
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author | Hawinkel, Stijn De Meyer, Sam Maere, Steven |
author_facet | Hawinkel, Stijn De Meyer, Sam Maere, Steven |
author_sort | Hawinkel, Stijn |
collection | PubMed |
description | Naturally occurring variability within a study region harbors valuable information on relationships between biological variables. Yet, spatial patterns within these study areas, e.g., in field trials, violate the assumption of independence of observations, setting particular challenges in terms of hypothesis testing, parameter estimation, feature selection, and model evaluation. We evaluate a number of spatial regression methods in a simulation study, including more realistic spatial effects than employed so far. Based on our results, we recommend generalized least squares (GLS) estimation for experimental as well as for observational setups and demonstrate how it can be incorporated into popular regression models for high-dimensional data such as regularized least squares. This new method is available in the BioConductor R-package pengls. Inclusion of a spatial error structure improves parameter estimation and predictive model performance in low-dimensional settings and also improves feature selection in high-dimensional settings by reducing “red-shift”: the preferential selection of features with spatial structure. In addition, we argue that the absence of spatial autocorrelation (SAC) in the model residuals should not be taken as a sign of a good fit, since it may result from overfitting the spatial trend. Finally, we confirm our findings in a case study on the prediction of winter wheat yield based on multispectral measurements. |
format | Online Article Text |
id | pubmed-9006620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90066202022-04-14 Spatial Regression Models for Field Trials: A Comparative Study and New Ideas Hawinkel, Stijn De Meyer, Sam Maere, Steven Front Plant Sci Plant Science Naturally occurring variability within a study region harbors valuable information on relationships between biological variables. Yet, spatial patterns within these study areas, e.g., in field trials, violate the assumption of independence of observations, setting particular challenges in terms of hypothesis testing, parameter estimation, feature selection, and model evaluation. We evaluate a number of spatial regression methods in a simulation study, including more realistic spatial effects than employed so far. Based on our results, we recommend generalized least squares (GLS) estimation for experimental as well as for observational setups and demonstrate how it can be incorporated into popular regression models for high-dimensional data such as regularized least squares. This new method is available in the BioConductor R-package pengls. Inclusion of a spatial error structure improves parameter estimation and predictive model performance in low-dimensional settings and also improves feature selection in high-dimensional settings by reducing “red-shift”: the preferential selection of features with spatial structure. In addition, we argue that the absence of spatial autocorrelation (SAC) in the model residuals should not be taken as a sign of a good fit, since it may result from overfitting the spatial trend. Finally, we confirm our findings in a case study on the prediction of winter wheat yield based on multispectral measurements. Frontiers Media S.A. 2022-03-30 /pmc/articles/PMC9006620/ /pubmed/35432426 http://dx.doi.org/10.3389/fpls.2022.858711 Text en Copyright © 2022 Hawinkel, De Meyer and Maere. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Hawinkel, Stijn De Meyer, Sam Maere, Steven Spatial Regression Models for Field Trials: A Comparative Study and New Ideas |
title | Spatial Regression Models for Field Trials: A Comparative Study and New Ideas |
title_full | Spatial Regression Models for Field Trials: A Comparative Study and New Ideas |
title_fullStr | Spatial Regression Models for Field Trials: A Comparative Study and New Ideas |
title_full_unstemmed | Spatial Regression Models for Field Trials: A Comparative Study and New Ideas |
title_short | Spatial Regression Models for Field Trials: A Comparative Study and New Ideas |
title_sort | spatial regression models for field trials: a comparative study and new ideas |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006620/ https://www.ncbi.nlm.nih.gov/pubmed/35432426 http://dx.doi.org/10.3389/fpls.2022.858711 |
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