Package {TSDFGS}

Type: Package
Title: Training Set Determination for Genomic Selection
Version: 3.0.0
Encoding: UTF-8
Date: 2026-11-05
URL: https://www.oumark.net/TSDFGS/, https://github.com/oumarkme/TSDFGS
BugReports: https://github.com/oumarkme/TSDFGS/issues
Depends: R (≥ 4.0)
Description: Finds a training set for genomic selection and the size it needs. The r-score of Ou and Liao (2019) <doi:10.1007/s00122-019-03387-0> is derived from Pearson's correlation between the genomic estimated breeding values and the phenotypic values of the test set. optTrain() searches for a good training set, and SSDFGS() searches for its size. Eighteen criteria are available to score or optimize a training set, among them A-, D-, E- and G-optimality, PEVmax, MSPE, CDmean, CDmax, and genomic relationship criteria (Chen et al. (2024) <doi:10.1007/s00122-024-04766-y>; Fernandez-Gonzalez et al. (2023) <doi:10.1007/s00122-023-04265-6>). The computation uses 'Rcpp' and 'RcppEigen', and independent searches can run on several threads with 'OpenMP'.
License: GPL (≥ 3)
Imports: Rcpp, stats
LinkingTo: Rcpp, RcppEigen
Suggests: ggplot2, testthat (≥ 3.0.0)
SystemRequirements: C++17
LazyData: true
Config/roxygen2/version: 8.1.0
NeedsCompilation: yes
Packaged: 2026-11-11 03:05:11 UTC; user
Author: Jen-Hsiang Ou ORCID iD [aut, cre] (affiliation: Department of Medical Genetics, National Taiwan University Hospital), Po-Ya Wu ORCID iD [aut], Chen-Tuo Liao ORCID iD [aut, ths]
Maintainer: Jen-Hsiang Ou <oumark.me@outlook.com>
Repository: CRAN
Date/Publication: 2026-11-12 16:50:02 UTC

TSDFGS: Training Set Determination for Genomic Selection

Description

Finds a training set for genomic selection and the size it needs. The r-score of Ou and Liao (2019) <doi:10.1007/s00122-019-03387-0> is derived from Pearson's correlation between the genomic estimated breeding values and the phenotypic values of the test set. optTrain() searches for a good training set, and SSDFGS() searches for its size. Eighteen criteria are available to score or optimize a training set; see criteria().

Author(s)

Maintainer: Jen-Hsiang Ou oumark.me@outlook.com (ORCID) (affiliation: Department of Medical Genetics, National Taiwan University Hospital)

Authors:

See Also

Useful links:

Fit logistic growth curve model

Description

A function for fitting logisti growth model

Usage

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

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. If TRUE, the simulations run in parallel (OpenMP) on 75% of the available processors (limited by OMP_NUM_THREADS), or half of them when there are 4 or fewer.

threads

Default: NULL. The number of threads; it overrides multi.threads. See nt2r for the random numbers of the parallel run.

Value

Estimation of parameters.

Examples

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

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, threads = NULL)

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, the simulations run in parallel (OpenMP) on 75% of the available processors (limited by OMP_NUM_THREADS), or half of them when there are 4 or fewer.

threads

Default: NULL. The number of threads; it overrides multi.threads. See nt2r for the random numbers of the parallel run.

Value

A list with the operating curve and growth curve figures (OC.fig and GC.fig; ggplot objects, or NULL when the ggplot2 package is not installed), the logistic growth curve parameters, and the operating curve data (OC.fit).

Author(s)

Jen-Hsiang Ou & Po-Ya Wu

Examples

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

A-optimality score

Description

tr[(X'X + \lambda I)^{-1}]: the trace of the inverse ridge information matrix of the training set. Smaller is better. It uses the training set only.

Usage

aopt_score(X, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Akdemir, D., & Isidro-Sanchez, J. (2019). Design of training populations for selective phenotyping in genomic prediction. Scientific Reports, 9, 1446. <doi:10.1038/s41598-018-38081-6>

Examples

data(geno)
aopt_score(geno[1:50, ])

Avg_GRM_MinMax score

Description

Mean relationship between the training set and the target rows minus the mean relationship within the training set (a = 1, b = 1). Larger is better.

Usage

avg_grm_minmax_score(X, X0)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Fernandez-Gonzalez, J., Akdemir, D., & Isidro y Sanchez, J. (2023). A comparison of methods for training population optimization in genomic selection. Theoretical and Applied Genetics, 136, 30. <doi:10.1007/s00122-023-04265-6>

Examples

data(geno)
avg_grm_minmax_score(geno[1:50, ], geno[51:100, ])

Avg_GRM score

Description

Mean of the relationship matrix G = XX'/p between the training set and the target rows (a = 1, b = 0 of the generalized Avg_GRM). Larger is better.

Usage

avg_grm_score(X, X0)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Fernandez-Gonzalez, J., Akdemir, D., & Isidro y Sanchez, J. (2023). A comparison of methods for training population optimization in genomic selection. Theoretical and Applied Genetics, 136, 30. <doi:10.1007/s00122-023-04265-6>

Examples

data(geno)
avg_grm_score(geno[1:50, ], geno[51:100, ])

Avg_GRM_self score

Description

Mean of G = XX'/p within the training set. Smaller is better. It uses the training set only.

Usage

avg_grm_self_score(X)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Fernandez-Gonzalez, J., Akdemir, D., & Isidro y Sanchez, J. (2023). A comparison of methods for training population optimization in genomic selection. Theoretical and Applied Genetics, 136, 30. <doi:10.1007/s00122-023-04265-6>

Examples

data(geno)
avg_grm_self_score(geno[1:50, ])

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, lambda = NULL)

Arguments

X

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

X0

A numeric matrix. The test set genotypic information matrix, with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion (r-score and PEV: 1 / number of columns; CD: 1).

Value

A number, the CD score (larger is better).

Author(s)

Jen-Hsiang Ou

Examples

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

CDmax score

Description

Largest, over the target rows, of the coefficient of determination of the ridge model on the columns of X (the per-individual value averaged by cd_score; default \lambda = 1). It protects the best-determined individual, as the CDMAX of STPGA does. Larger is better.

Usage

cdmax_score(X, X0, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Akdemir, D., Sanchez, J. I., & Jannink, J.-L. (2015). Optimization of genomic selection training populations with a genetic algorithm. Genetics Selection Evolution, 47, 38. <doi:10.1186/s12711-015-0116-6>

Examples

data(geno)
cdmax_score(geno[1:50, ], geno[51:100, ])

CDmean(v2) score

Description

Mean of diag(K_{PT} W K_{TP}) / diag(K_{PP}), the heuristic form of CDmean (GBLUP, K = XX'/p, default \lambda = 1). Larger is better.

Usage

cdmean2_score(X, X0, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Chen, S.-P., Sung, W.-H., & Liao, C.-T. (2024). Constructing training sets for genomic selection to identify superior genotypes in candidate populations. Theoretical and Applied Genetics, 137, 270. <doi:10.1007/s00122-024-04766-y>

Examples

data(geno)
cdmean2_score(geno[1:50, ], geno[51:100, ])

CDmean score (GBLUP form)

Description

Mean coefficient of determination of the contrasts between each target row and the target mean, for a GBLUP model with K = XX'/p, a general mean as the only fixed effect, and penalty \lambda = \sigma_e^2/\sigma_A^2 (default 1). Larger is better.

Usage

cdmean_score(X, X0, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Rincent, R., et al. (2012). Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals. Genetics, 192, 715-728. <doi:10.1534/genetics.112.141473> Chen, S.-P., Sung, W.-H., & Liao, C.-T. (2024). Constructing training sets for genomic selection to identify superior genotypes in candidate populations. Theoretical and Applied Genetics, 137, 270. <doi:10.1007/s00122-024-04766-y>

Examples

data(geno)
cdmean_score(geno[1:50, ], geno[51:100, ])

List of training set criteria

Description

Returns the criteria that optTrain can optimize, with the name to give to its method argument, the function that scores a given training set, the direction of optimization, and whether the criterion uses a test set.

Usage

criteria()

Details

Criteria marked uses_test = FALSE depend on the training set only. When a test set is given to optTrain, such a criterion ignores it (with a warning). One page per criterion on the package website gives the definition, the original reference, and the license review.

Value

A data frame with the columns method, name, type, score_fun, maximize, and uses_test.

Author(s)

Jen-Hsiang Ou

Examples

criteria()

D-optimality score

Description

\log\det(X'X + \lambda I) of the training set. Larger is better (a larger determinant of the information matrix). It uses the training set only.

Usage

dopt_score(X, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Akdemir, D., & Isidro-Sanchez, J. (2019). Design of training populations for selective phenotyping in genomic prediction. Scientific Reports, 9, 1446. <doi:10.1038/s41598-018-38081-6>

Examples

data(geno)
dopt_score(geno[1:50, ])

E-optimality score

Description

Largest eigenvalue of (X'X + \lambda I)^{-1}, that is 1 / (\lambda_{min}(X'X) + \lambda). Smaller is better. It uses the training set only. When X has more columns than rows, X'X is singular and the score is 1/\lambda for every training set.

Usage

eopt_score(X, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Akdemir, D., & Isidro-Sanchez, J. (2019). Design of training populations for selective phenotyping in genomic prediction. Scientific Reports, 9, 1446. <doi:10.1038/s41598-018-38081-6>

Examples

data(geno)
eopt_score(geno[1:50, ])

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)

G-optimality score (GOPTPEV)

Description

Largest eigenvalue of the prediction error variance matrix of the target rows, X_0 (X'X + \lambda I)^{-1} X_0', with an intercept column added. Smaller is better.

Usage

gopt_score(X, X0, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Akdemir, D., & Isidro-Sanchez, J. (2019). Design of training populations for selective phenotyping in genomic prediction. Scientific Reports, 9, 1446. <doi:10.1038/s41598-018-38081-6>

Examples

data(geno)
gopt_score(geno[1:50, ], geno[51:100, ])

GVaverage score

Description

tr(K_T) with K_T = XX'/p (average genomic variation). Larger is better. It uses the training set only.

Usage

gv_average_score(X)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Chen, S.-P., Sung, W.-H., & Liao, C.-T. (2024). Constructing training sets for genomic selection to identify superior genotypes in candidate populations. Theoretical and Applied Genetics, 137, 270. <doi:10.1007/s00122-024-04766-y>

Examples

data(geno)
gv_average_score(geno[1:50, ])

GVoverall score

Description

\log\det K_T with K_T = XX'/p, the relationship matrix of the training set (overall genomic variation). Larger is better. It needs at least as many columns as rows, otherwise K_T is singular and the score is minus infinity.

Usage

gv_overall_score(X)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Chen, S.-P., Sung, W.-H., & Liao, C.-T. (2024). Constructing training sets for genomic selection to identify superior genotypes in candidate populations. Theoretical and Applied Genetics, 137, 270. <doi:10.1007/s00122-024-04766-y>

Examples

data(geno)
gv_overall_score(geno[1:50, ])

MSPE score of ridge regression

Description

The approximation of the expected mean squared prediction error of ridge regression, 1 + tr[X_0 A A' X_0']/n_0 + tr[(X_0 - X_0 A X)'(X_0 - X_0 A X)] with A = X'(XX' + \lambda I)^{-1}. Smaller is better. The default penalty is \lambda = 1.

Usage

mspe_score(X, X0, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Chen, S.-P., Sung, W.-H., & Liao, C.-T. (2024). Constructing training sets for genomic selection to identify superior genotypes in candidate populations. Theoretical and Applied Genetics, 137, 270. <doi:10.1007/s00122-024-04766-y>

Examples

data(geno)
mspe_score(geno[1:50, ], geno[51:100, ])

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, threads = NULL)

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. If TRUE, the iterations run in parallel (OpenMP, any system with OpenMP support) on 75% of the available processors (limited by OMP_NUM_THREADS), or half of them when there are 4 or fewer.

threads

Default: NULL. The number of threads; it overrides multi.threads. The parallel iterations use their own random generators, seeded from R's generator. The values then depend on set.seed() but not on threads, and they are not those of the serial run with the same seed.

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,
  seed = NULL,
  lambda = NULL,
  threads = NULL
)

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. Without test, it has one entry per candidate (same order as cand); with test, it has one entry per row of geno.

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

A criterion name; see criteria for the list. "rScore" (default), "PEV" and "CD" are the criteria of v2.4.2.

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.

seed

Default: NULL. An integer passed to set.seed before the search, which changes the global random-number state. Leave it NULL to use the current state.

lambda

Default: NULL. A positive ridge penalty; NULL uses the default of each criterion (see criteria).

threads

Default: NULL (one thread). The number of threads that Eigen may use inside the matrix products of one search (OpenMP). For the exchange search it only helps when geno has many columns (several hundred; about 1.3 to 1.5 times faster at 404 columns on 2 threads) and the training set found is the same apart from rounding in the last digits. For the targeted r-score and PEV search without sub-populations (a genetic algorithm), the 30 candidate sets of each generation are scored in parallel, and the result does not depend on threads. To run many independent searches in parallel use SSDFGS.

Details

All twelve search routes of v2.4.2 (with or without sub-populations, targeted or un-targeted, and the three criteria) are kept. The search moves, their random-number order, the default numbers of iterations, and the stopping rule are the same as in v2.4.2, so that a given seed returns the training set of v2.4.2 (apart from the float-to-double change of the scores, see Details of r_score). The r-score is the criterion of Ou and Liao (2019) <doi:10.1007/s00122-019-03387-0>.

Only the r-score, PEV, and CD are tested carefully: they reproduce the output recorded from v2.4.2 on all twelve routes. The 15 criteria added in v3 follow their papers and run on all routes, but they are not carefully tested; evaluate them on your own data before relying on them.

Value

This function will return a list with OPTtrain (a vector of chosen optimal training set), TOPscore (highest scores of before iteration), ITERscore (criteria scores of each iteration), n.iter (number of iterations run), and converged (TRUE if the search stopped by the convergence rule, FALSE if it stopped at twice min.iter).

Author(s)

Jen-Hsiang Ou

References

Ou, J.-H., & Liao, C.-T. (2019). Training set determination for genomic selection. Theoretical and Applied Genetics, 132, 2781-2792. <doi:10.1007/s00122-019-03387-0>

Examples

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

PAM (k-medoids) cost

Description

Sum, over the target rows, of the Euclidean distance to the nearest training row: the cost that partitioning around medoids minimizes when the training rows are the medoids. Smaller is better.

Usage

pam_score(X, X0)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: an introduction to cluster analysis. Wiley. <doi:10.1002/9780470316801> Guo, T., et al. (2019). Optimal designs for genomic selection in hybrid crops. Molecular Plant, 12, 390-401. <doi:10.1016/j.molp.2018.12.022> Rio, S., et al. (2021). Genomic prediction and training set optimization in a structured Mediterranean oat population. Theoretical and Applied Genetics, 134, 3595-3609. <doi:10.1007/s00122-021-03916-w>

Examples

data(geno)
pam_score(geno[1:50, ], geno[51:100, ])

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, lambda = NULL)

Arguments

X

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

X0

A numeric matrix. The test set genotypic information matrix, with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion (r-score and PEV: 1 / number of columns; CD: 1).

Value

A number, the PEV score (smaller is better).

Author(s)

Jen-Hsiang Ou

Examples

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

PEVmax score

Description

Largest prediction error variance among the target rows, with an intercept column added: \max_i x_{0i}' (X'X + \lambda I)^{-1} x_{0i}. Smaller is better.

Usage

pevmax_score(X, X0, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Details

See criteria for the list of criteria and the package website for one page per criterion.

Value

A number.

Author(s)

Jen-Hsiang Ou

References

Akdemir, D., & Isidro-Sanchez, J. (2019). Design of training populations for selective phenotyping in genomic prediction. Scientific Reports, 9, 1446. <doi:10.1038/s41598-018-38081-6>

Examples

data(geno)
pevmax_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, lambda = NULL)

Arguments

X

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

X0

A numeric matrix. The test set genotypic information matrix, with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion (r-score and PEV: 1 / number of columns; CD: 1).

Details

The value equals the r-score of v2.4.2 (to its single-precision accuracy of about 1e-7). Since v3 the computation uses p x p matrices when the number of columns p is at most the training set size n, and n x n matrices otherwise, and the result is returned in double precision. The term 1 - 1/n0 (n0: test set size) of v2.4.2 is kept as published in the software; see NEWS.md.

Value

A number, the r-score.

Author(s)

Jen-Hsiang Ou

References

Ou, J.-H., & Liao, C.-T. (2019). Training set determination for genomic selection. Theoretical and Applied Genetics, 132, 2781-2792. <doi:10.1007/s00122-019-03387-0>

Examples

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

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)