R/predict_cv2.R
predict_cv2.Rd
Get train/test splits of the phenotypic MET dataset based on a number of random k-folds partitions determined by the user, according to the type CV2. Creation of the list of train/test splits based on phenotypic data, so that all the Year x Location phenotypic observations from the phenotypic MET dataset are assigned randomly to k-fold partitions (prediction of incomplete field trials).
predict_cv2(pheno_data, nb_folds, reps, seed)
pheno_data |
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nb_folds |
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reps |
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a cv_object
object which contains nb_folds x reps elements.
Each element of the object corresponds to a split
object with two
elements:
data.frame
Dataset with all observations for the
training set.
data.frame
Dataset with all observations for the test
set.
Jarqu攼㹤n D, Lemes da Silva C, Gaynor RC, Poland J, Fritz A, Howard R, Battenfield S, Crossa J (2017). “Increasing genomic-enabled prediction accuracy by modeling genotype\(\times\) environment interactions in Kansas wheat.” The plant genome, 10(2), 1--15. Jarqu攼㹤n D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, P攼㸹rez P, Calus M, others (2014). “A reaction norm model for genomic selection using high-dimensional genomic and environmental data.” Theoretical and applied genetics, 127(3), 595--607.
Cathy C. Westhues cathy.jubin@uni-goettingen.de