cv_fast {predhy} | R Documentation |
Evaluate Trait Predictability via the HAT Method
Description
The HAT method is a fast algorithm for the ordinary cross validation. It is highly recommended for large dataset (Xu et al. 2017).
Usage
cv_fast(fix = NULL, y, kk, nfold = 5, seed = 123)
Arguments
fix |
a design matrix of the fixed effects. If not passed, a vector of ones is added for the intercept. |
y |
a vector of the phenotypic values. |
kk |
a list of one or multiple kinship matrices. |
nfold |
the number of folds, default is 5. For the HAT Method, nfold can be set as the sample size (leave-one-out CV) to avoid
variation caused by random partitioning of the samples, but it is not recommended for |
seed |
the random number, default is 123. |
Value
Trait predictability
References
Xu S. (2017) Predicted residual error sum of squares of mixed models: an application for genomic prediction. G3 (Bethesda) 7, 895-909.
Examples
## load example data from hypred package
data(hybrid_phe)
data(input_geno)
## convert original genotype
inbred_gen <- convertgen(input_geno, type = "hmp2")
## infer the additive and dominance genotypes of hybrids
gena <- infergen(inbred_gen, hybrid_phe)$add
gend <- infergen(inbred_gen, hybrid_phe)$dom
## calculate the additive and dominance kinship matrix
ka <- kin(gena)
kd <- kin(gend)
##for the additive model
predictability <- cv_fast(y = hybrid_phe[,3], kk = list(ka))
##for the additive-dominance model
predictability <- cv_fast(y = hybrid_phe[,3], kk = list(ka,kd))
[Package predhy version 2.1.1 Index]