predict_selection_score

Provides tools for the simultaneous improvement of multiple traits in plant breeding. Building upon the classical selection index (Smith 1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) and modern quantitative genetics (Kang 2020 <doi:10.1007/978-3-319-91223-3>), this package calculates classical phenotypic, genomic, marker-assisted, restricted/constrained, and eigen selection indices. It also incorporates multi-stage selection evaluation and stochastic simulations to estimate genetic advance based on economic weights, heritability, and genetic correlations.

Zankrut Goyani

selection.index

Analysis of Selection Index in Plant Breeding

predict_selection_score function

<dl><dt>index_df</dt>
<dd>Data frame returned by lpsi()</dd>
<dt>data</dt>
<dd>Raw phenotypic data matrix/data frame (observations x traits)</dd>
<dt>genotypes</dt>
<dd>Vector of genotype/treatment labels for each observation</dd></dl>

Arguments

Predict selection index scores — predict_selection_score

<dl>

<dt>index_df</dt>
<dd>Data frame returned by lpsi()</dd>


<dt>data</dt>
<dd>Raw phenotypic data matrix/data frame (observations x traits)</dd>


<dt>genotypes</dt>
<dd>Vector of genotype/treatment labels for each observation</dd>

</dl>

predict_selection_score: Predict selection index scores

Description

Usage

Value

Arguments

Examples