corr_ss

The magnitude of the correlation coefficient.

The half-width for confidence interval at p &lt; 0.05.

Logical argument. If <code>verbose = FALSE</code> the code is run
silently.

verbose

Find the required (sufficient) sample size for computing a Pearson
correlation coefficient with a desired confidence interval (Olivoto et al.,
2018).

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>,
Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang
(2003) <doi:10.1201/9781420040371>, joint Regression Analysis by
Eberhart & Russel (1966)
(<doi:10.2135/cropsci1966.0011183X000600010011x>), ecovalence by
Wricke (1965), genotypic confidence index by Annicchiarico (1992),
Murakami & Cruz's (2004) method <doi:10.12702/1984-7033.v04n01a02>,
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>, geometric
adaptability index described by Shahbazi (2019)
<doi:10.1016/j.scienta.2019.04.047>. 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.

corr_ss: Sample size planning for a desired Pearson's correlation confidence interval

Description

Usage

Arguments

Details

References

Examples