Spatial Regression Models for Field Trials: A Comparative Study and New Ideas.
cross-validation
feature selection
field trial
generalized least squares
prediction
simulation
spatial autocorrelation
Journal
Frontiers in plant science
ISSN: 1664-462X
Titre abrégé: Front Plant Sci
Pays: Switzerland
ID NLM: 101568200
Informations de publication
Date de publication:
2022
2022
Historique:
received:
20
01
2022
accepted:
17
02
2022
entrez:
18
4
2022
pubmed:
19
4
2022
medline:
19
4
2022
Statut:
epublish
Résumé
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
Identifiants
pubmed: 35432426
doi: 10.3389/fpls.2022.858711
pmc: PMC9006620
doi:
Types de publication
Journal Article
Langues
eng
Pagination
858711Informations de copyright
Copyright © 2022 Hawinkel, De Meyer and Maere.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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