Package 'TSDFGS'

CD-score

Description

This function calculate CD-score <doi:10.1186/1297-9686-28-4-359> by given training set and test set.

Usage

cd_score(X, X0)

Arguments

X	A numeric matrix. The training set genotypic information matrix can be given as genotype matrix (coded as -1, 0, 1) or principle component matrix (row: sample; column: marker).
X0	A numeric mareix. The test set genotypic information matrix can be given as genotype matrix (coded as -1, 0, 1) or principle component matrix (row: sample; column: marker).

Value

A floating-point number, CD score.

Author(s)

Jen-Hsiang Ou

Examples

data(geno)
## Not run: cd_score(geno[1:50, ], geno[51:100])

---

Fit logistic growth curve model

Description

A function for fitting logisti growth model

Usage

FGCM(geno, nt = NULL, n_iter = NULL, multi.threads = FALSE)

Arguments

geno	Genotype information saved as a dataframe. Columns represent variants (SNPs or PCs).
nt	A numerical vector of training set sample size for estimating logistic growth curve parameters
n_iter	Number of simulation of each training set size. Automatically gave a suitable number by default.
multi.threads	Default: FALSE. Multi-thread function is only avalyble for mac or linux systems.

Value

Estimation of parameters.

Examples

data(geno)
## Not run: FGCM(geno)

---

Genotype information

Description

A PCA matrix of rice genotype information. This data was published by Zhao et al. (2011) <doi:10.1038/ncomms1467>

Usage

geno

Format

A numeric matrix (PCA) with 404 rows (sample) and 404 columns (PCs).

Source

http://www.ricediversity.org/data/

Examples

data(geno)

---

Simulate r-scores of each training set size

Description

Calculate r-scores (un-target) by in parallel.

Usage

nt2r(geno, nt, n_iter = 30, multi.threads = FALSE)

Arguments

geno	A numeric dataframe of genotype, column represent sites (genotype coding as 1, 0, -1)
nt	Numeric. Number of training set size
n_iter	Times of iteration. (default = 30)
multi.threads	Default: FALSE. Multi-thread function is only avalyble for mac or linux systems.

Value

A vector of r-scores of each iteration

Examples

data(geno)
## Not run: nt2r(geno, 50)

---

Optimal training set determination

Description

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(s)

Jen-Hsiang Ou

Examples

data(geno)
## Not run: optTrain(geno, cand = 1:404, n.train = 100)

---

PEV score

Description

This function calculate prediction error variance (PEV) score <doi:10.1186/s12711-015-0116-6> by given training set and test set.

Usage

pev_score(X, X0)

Arguments

X	A numeric matrix. The training set genotypic information matrix can be given as genotype matrix (coded as -1, 0, 1) or principle component matrix (row: sample; column: marker).
X0	A numeric mareix. The test set genotypic information matrix can be given as genotype matrix (coded as -1, 0, 1) or principle component matrix (row: sample; column: marker).

Value

A floating-point number, PEV score.

Author(s)

Jen-Hsiang Ou

Examples

data(geno)
## Not run: pev_score(geno[1:50, ], geno[51:100])

---

r-score

Description

This function calculate r-score <doi:10.1007/s00122-019-03387-0> by given training set and test set.

Usage

r_score(X, X0)

Arguments

X	A numeric matrix. The training set genotypic information matrix can be given as genotype matrix (coded as -1, 0, 1) or principle component matrix (row: sample; column: marker).
X0	A numeric mareix. The test set genotypic information matrix can be given as genotype matrix (coded as -1, 0, 1) or principle component matrix (row: sample; column: marker).

Value

A floating-point number, r-score.

Author(s)

Jen-Hsiang Ou

Examples

data(geno)
## Not run: r_score(geno[1:50, ], geno[51:100])

---

Sample size determination for genomic selection

Description

This function is designed to generate an operating curve for sample size determination

Usage

SSDFGS(geno, nt = NULL, n_iter = NULL, multi.threads = FALSE)

Arguments

geno	A numeric data frame carried genotype information (column: PCs, row: sample)
nt	A numeric vector carried training set sizes for r-score simulation.
n_iter	Number of iterations for estimating parameters.
multi.threads	Default: False. If TRUE, this function will use 75% of threads if the computer has more than 4 threads. Multi-thread computing is only avalible in mac and linux environments.

Value

An operating curve and its information.

Author(s)

Jen-Hsiang Ou & Po-Ya Wu

Examples

data(geno)
## Not run: SSDFGS(geno)

---

Sub-population information

Description

Sub-population information of samples. This data was published by Zhao et al. (2011) <doi:10.1038/ncomms1467>

Usage

subpop

Format

A character vector.

Source

http://www.ricediversity.org/data/

Examples

data(subpop)

---

TSDFGS

Description

We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.

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