optTrain {TSDFGS}R Documentation

Algorithm for optimal training set determination

Description

It uses a genetic algorithm or simple exchange algorithm with three different criteria (r-score (J.H. Ou et al., (2019) <DOI:10.6342/NTU201802290>), PEV-score (Akdemir D. et al., (2015) <DOI:10.1186/s12711-015-0116-6>), CD-score (Laloe D. (1993) <DOI:10.1186/1297-9686-25-6-557>)) to determine an optimal training set.

Usage

optTrain(geno, cand, n.train, subpop = NULL, 
        test = NULL, method = "rScore", min.iter = NULL)

Arguments

geno

A numeric matrix of principal components (rows: individuals; columns: PCs). To reduce computing time, one may use first k PCs by geno[,1:k].

cand

An integer vector of which rows of individuals are candidates of the training set in the geno matrix.

n.train

The size of the target training set.

subpop

A character vector of subpopulation's group name. The algorithm will ignore the population structure if it remains NULL.

test

An integer vector of which rows of individuals are in the test set in the geno matrix. The algorithm will use an un-target method if it remains NULL.

method

Choices are rScore, PEV and CD. rScore will be used by default.

min.iter

Minimum iteration of all methods can be appointed. One should always check if the algorithm is converged or not. A minimum iteration will set by considering the candidate and test set size if it remains NULL.

Value

OPTtrain

An integer vector of the chosen optimal training set.

TOPscore

Score of each iteration. (Given by one of three criterions)

ITERscore

Score of the best solution in by far. (Given by one of three criterions.)

Note

Both genetic algorithm and simple exchange algorithms do not assure convergence to global optimal, and it is highly recommended to draw the convergence plot to check it converges to the local optimal.

Author(s)

Jen-Hsiang Ou and Chen-Tuo Liao

Maintainer: Jen-Hsiang Ou<oumark.me@outlook.com>

References

Akdemir D., Sanchez JI., Jannink JL. (2015), Optimization of genomic selection training populations with a genetic algorithm. GenSetic Selection Evolution 47:38.\

Laloe D. (1993), Precision and information in linear models of genetic evolution. Genetics Selection Evolution 25:557.\

Holland J. H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press.

Examples

  
    ## LOAD EXAMPLE DATA ##
    data("rice44kPCA")
    out.RNN = optTrain(geno, cand = 1:404, n.train = 100)
   

[Package TSDFGS version 1.0 Index]