reorder_cormat

<a href="https://lifecycle.r-lib.org/articles/stages.html#stable"><img src="figures/lifecycle-stable.svg?package=metan&version=1.15.0" alt="[Stable]"></a>
Reorder the correlation matrix according to the correlation coefficient by
using hclust for hierarchical clustering order. This is useful to identify
the hidden pattern in the matrix.

Performs stability analysis of multi-environment
trial data using parametric and non-parametric methods. Parametric
methods includes Additive Main Effects and Multiplicative Interaction
(AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>,
Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE)
biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>,
geometric adaptability index by Mohammadi & Amri (2008)
<doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart
& Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>,
genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's
(2004) method, power law residuals
(POLAR) statistics by Doring et al. (2015)
<doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of
variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>,
stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>,
weighted average of absolute scores by Olivoto et al. (2019a)
<doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by
Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>.
Non-parametric methods includes superiority index by Lin & Binns
(1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic
stability by Huehn (1990)
<https://link.springer.com/article/10.1007/BF00024241>, TOP third
statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for
computing biometrical analysis such as path analysis, canonical
correlation, partial correlation, clustering analysis, and tools for
inspecting, manipulating, summarizing and plotting typical
multi-environment trial data are also provided.

Tiago Olivoto

metan

Multi Environment Trials Analysis

reorder_cormat function

<a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>
Reorder the correlation matrix according to the correlation coefficient by
using hclust for hierarchical clustering order. This is useful to identify
the hidden pattern in the matrix.

reorder_cormat: Reorder a correlation matrix

Description

Usage

Arguments

Value

Examples