Optimal training set determination

This function is designed for determining optimal training set.

Usage

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

Arguments

geno

A numeric matrix of principal components (rows: individuals; columns: PCs).

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. This could be determined with the help of the ssdfgp function provided in this package.

subpop

A character vector of sub-population'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.

console

Default: TRUE. Set it to FALSE if you don't want the function printing out the number count of each iteration.

Value

This function will return 3 information including OPTtrain (a vector of chosen optimal training set), TOPscore (highest scores of before iteration), and ITERscore (criteria scores of each iteration).

Author

Jen-Hsiang Ou

Examples

data(geno)
if (FALSE) optTrain(geno, cand = 1:404, n.train = 100)