selectiongain: A Tool for Calculation and Optimization of the Expected Gain from Multi-Stage Selection
Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, 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 plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.
- Xuefei Mi, Jose Marulanda, H. Friedrich Utz, Albrecht E. Melchinger (Project contact person: Melchinger@uni-hohenheim.de )
- Date of publication
- 2016-10-09 17:54:53
- Xuefei Mi <firstname.lastname@example.org>
- Function for calculating correlation matrix in a plant...
- Function for calculating the expected multi-stage selection...
- Function for calculating the selection gain in each stage
- Function for optimizing multi-stage selection with grid...
- Function for optimizing n-stage selection with the NLM...
- Function for optimizing three-stage selection in plant...
- Function for optimizing four-stage selection in plant...
- Function for calculating the truncation points
- Expected variance after selection after k stages selection
Files in this package