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

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

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).

Saved in:
Bibliographic Details
Main Authors: Montesinos-López, Abelardo, Montesinos-López, Osval A., Montesinos-López, José Cricelio, Flores-Cortes, Carlos Alberto, de la Rosa, Roberto, Crossa, Jose
Other Authors: Dreher, Kate
Format: Experimental data biblioteca
Language:English
Published: CIMMYT Research Data & Software Repository Network 2020
Subjects:Agricultural Sciences, Agricultural research, Sparse kernel methods, Grain yield, Days to heading, Wheat, Triticum aestivum,
Online Access:https://hdl.handle.net/11529/10548532
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://hdl.handle.net/11529/10548532

Similar Items