int.effects

data

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.

int.effects: Data for examples

Description

Arguments