hybrid_phe

This dataset contains phenotypic data of 410 hybrids for grain yield in maize.

datasets

Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM.
GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine.
It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
A complete manual for this package is provided in the manual folder of the package installation directory.
You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI").

Yuxiang Zhang

predhy.GUI

Genomic Prediction of Hybrid Performance with Graphical User
Interface

Yang Xu

Guangning Yu

Yanru Cui

Shizhong Xu

Chenwu Xu

hybrid_phe function

A data frame with 410 rows and 3 variables:<dl>
 <dt><code>M</code></dt>
<dd>The names of male parents.</dd> <dt><code>F</code></dt>
<dd>The names of female parents.</dd> <dt><code>GY</code></dt>
<dd>The grain yield of hybrids.</dd>
</dl>

Format

Phenotypic data of hybrids — hybrid_phe

A data frame with 410 rows and 3 variables:<dl>
 <dt><code>M</code></dt>
<dd>The names of male parents.</dd>

 <dt><code>F</code></dt>
<dd>The names of female parents.</dd>

 <dt><code>GY</code></dt>
<dd>The grain yield of hybrids.</dd>


</dl>

hybrid_phe: Phenotypic data of hybrids

Description

Usage

Arguments

Format