cv {predhy}

Evaluate Trait Predictability via Cross Validation

Description

The cv function evaluates trait predictability based on eight GS methods via k-fold cross validation. The trait predictability is defined as the squared Pearson correlation coefficient between the observed and the predicted trait values.

Usage

cv(
  fix = NULL,
  gena,
  gend = NULL,
  parent_phe = NULL,
  hybrid_phe,
  method = "GBLUP",
  drawplot = TRUE,
  nfold = 5,
  nTimes = 1,
  seed = 1234,
  CPU = 1
)

Arguments

fix

a design matrix of the fixed effects.

gena

a matrix (n x m) of additive genotypes for the training population.

gend

a matrix (n x m) of domiance genotypes for the training population. Default is NULL.

parent_phe

a matrix of a phenotypic values of parent.The names parent_phe must match the rownames of inbred_gen. Default is NULL.

hybrid_phe

a data frame with three columns. The first column and the second column are the names of male and female parents of the corresponding hybrids, respectively; the third column is the phenotypic values of hybrids. The names of male and female parents must match the rownames of inbred_gen. Missing (NA) values are not allowed.

method

eight GS methods including "GBLUP", "BayesB", "RKHS", "PLS", "LASSO", "EN", "XGBoost","LightGBM". Users may select one of these methods or all of them simultaneously with "ALL". Default is "GBLUP".

drawplot

when method ="ALL", user may select TRUE for a barplot about eight GS methods. Default is TRUE.

nfold

the number of folds. Default is 5.

nTimes

the number of independent replicates for the cross-validation. Default is 1.

seed

the random number. Default is 1234.

CPU

the number of CPU.

Value

Trait predictability

Examples

## load example data from predhy package
data(hybrid_phe)
data(input_geno)

## convert original genotype
inbred_gen <- convertgen(input_geno, type = "hmp2")

##additive model infer the additive and dominance genotypes of hybrids
gena <- infergen(inbred_gen, hybrid_phe)$add
gend <- infergen(inbred_gen, hybrid_phe)$dom

##additive model
R2<-cv(fix=NULL,gena,gend=NULL,parent_phe=NULL,hybrid_phe,method ="GBLUP")

##additive-dominance model
R2<-cv(fix=NULL,gena,gend,parent_phe=NULL,hybrid_phe,method ="GBLUP")