Name
Abstract | ||
|---|---|---|
Multi-stage selection is practised in numerous fields of the life sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and efforts, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain $$\\varDelta G(y)$$ΔG(y) plays a central role. It can be calculated by integration of a truncated multivariate normal distribution. While mathematical formulas for calculating $$\\varDelta G(y)$$ΔG(y) and $$\\psi (y)$$¿(y), the variance among the selected candidates, were developed a long time ago, solutions and software for numerical calculations were not available. We developed the R package selectiongain for efficient and precise calculation of $$\\varDelta G(y)$$ΔG(y) and $$\\psi (y)$$¿(y) for (i) a given matrix $$\\varvec{\\varSigma }^{*}$$Σ¿ of correlations among the unobservable target character and the selection criteria and (ii) given coordinates $$\\mathbf Q $$Q of the truncation point or the selected fractions $$\\varvec{\\alpha }$$¿ in each stage. In addition, our software can be used for optimizing multi-stage selection programs under a given total budget and different costs of evaluating the candidates in each stage. Besides a detailed description of the functions of the software, the package is illustrated with two examples. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00180-015-0583-9 | Computational Statistics |
Keywords | DocType | Volume |
Selection gain, Multivariate normal integral, Optimal allocations | Journal | 31 |
Issue | ISSN | Citations |
2 | 1613-9658 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| xuefei mi | 1 | 0 | 0.68 |
| h friedrich utz | 2 | 0 | 0.34 |
| A E Melchinger | 3 | 2 | 0.74 |