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selectiongain (version 2.0.710)

A Tool for Calculation and Optimization of the Expected Gain from Multi-Stage Selection

Description

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.

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Version

Install

install.packages('selectiongain')

Monthly Downloads

260

Version

2.0.710

License

GPL-2

Maintainer

Xuefei Mi

Last Published

September 17th, 2022

Functions in selectiongain (2.0.710)

multistageoptimum.searchIndexT

Function for optimizing three-stage selection in plant breeding with one marker-assisted selection stage and two phenotypic selection stages
multistagegain

Function for calculating the expected multi-stage selection gain
multistageoptimum.grid

Function for optimizing multi-stage selection with grid algorithm for a given correlation matrix
multistageoptimum.search

Function for optimizing three-stage selection in plant breeding with one marker-assisted selection stage and two phenotypic selection stages
multistageoptimum.searchThreeS

Function for optimizing four-stage selection in plant breeding with one marker-assisted selection stage and three phenotypic selection stages
multistagecor

Function for calculating correlation matrix in a plant breeding context
multistageoptimum.nlm

Function for optimizing n-stage selection with the NLM algorithm for a given correlation matrix
SDselectiongain

Function for calculating the standrd deviation of selection gain
multistagevariance

Expected variance after selection after k stages selection
multistagegain.each

Function for calculating the selection gain in each stage
multistagetp

Function for calculating the truncation points