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Replication Data for: A guide for generalized kernel regression methods for genomic-enabled prediction

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: A guide for generalized kernel regression methods for genomic-enabled prediction
 
Identifier https://hdl.handle.net/11529/10548532
 
Creator Montesinos-López, Abelardo
Montesinos-López, Osval A.
Montesinos-López, José Cricelio
Flores-Cortes, Carlos Alberto
de la Rosa, Roberto
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description The data contained in these datasets can be used to implement Bayesian generalized kernel regression methods for genome-enabled prediction in the statistical software R, The accompanying paper describes the building process of 7 kernel methods (linear, polynomial, sigmoid, Gaussian and Arc-cosine 1, Arc-cosine L).
 
Subject Agricultural Sciences
Agricultural research
Sparse kernel methods
Grain yield
Days to heading
Wheat
Triticum aestivum
 
Language English
 
Date 2020
 
Contributor Dreher, Kate
Bill and Melinda Gates Foundation (BMGF)
United States Agency for International Development (USAID)
CGIAR Research Program on Wheat (WHEAT)
CGIAR Research Program on Maize (MAIZE)
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund (JA)
Genetic Resources Program (GRP)
Global Wheat Program (GWP)
 
Type Experimental data
Genotypic data
Phenotypic data