corr_focus

Select a set of variables from a correlation matrix to keep as the columns,
and exclude these or all other variables from the rows.

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) <doi: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

corr_focus function

<dl><dt>model</dt>
<dd>A model computed with <code>corr_coef()</code> or a symmetric matrix, often
produced with <code><a href="/link/stats%3A%3Acor()?package=metan&version=1.19.0" data-mini-rdoc="metan::stats::cor()">stats::cor()</a></code>.</dd>
<dt>...</dt>
<dd>One or more unquoted variable name separated by commas. Variable
names can be used as if they were positions in the data frame, so
expressions like <code>x:y</code> can be used to select a range of variables.</dd></dl>

Arguments

Focus on section of a correlation matrix — corr_focus

<dl>

<dt>model</dt>
<dd>A model computed with <code>corr_coef()</code> or a symmetric matrix, often
produced with <code><a href='https://rdrr.io/r/stats/cor.html'>stats::cor()</a></code>.</dd>


<dt>...</dt>
<dd>One or more unquoted variable name separated by commas. Variable
names can be used as if they were positions in the data frame, so
expressions like <code>x:y</code> can be used to select a range of variables.</dd>

</dl>

corr_focus: Focus on section of a correlation matrix

Description

Usage

Value

Arguments

Examples