corr_ss

<a href="https://lifecycle.r-lib.org/articles/stages.html#stable"><img src="figures/lifecycle-stable.svg?package=metan&version=1.18.0" alt="[Stable]"></a>
Find the required (sufficient) sample size for computing a Pearson
correlation coefficient with a desired confidence interval (Olivoto et al.,
2018) as followsn = [ CI_w0.45304^r 2.25152 ]^ - 0.50089
where CI_w is desired confidence interval and r is the
correlation coefficient.

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

corr_ss function

<dl><dt>r</dt>
<dd>The magnitude of the correlation coefficient.</dd>
<dt>CI</dt>
<dd>The half-width for confidence interval at p &lt; 0.05.</dd>
<dt>verbose</dt>
<dd>Logical argument. If <code>verbose = FALSE</code> the code is run
silently.</dd></dl>

Arguments

Tiago Olivoto <a href="/link/tiagoolivoto%40gmail.com?package=metan&version=1.18.0" data-mini-rdoc="metan::tiagoolivoto@gmail.com">tiagoolivoto@gmail.com</a>

Author

<a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>
Find the required (sufficient) sample size for computing a Pearson
correlation coefficient with a desired confidence interval (Olivoto et al.,
2018) as follows

n = [ CI_w0.45304^r 2.25152 ]^ - 0.50089
where CI_w is desired confidence interval and r is the
correlation coefficient.

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

<dl>

<dt>r</dt>
<dd>The magnitude of the correlation coefficient.</dd>


<dt>CI</dt>
<dd>The half-width for confidence interval at p &lt; 0.05.</dd>


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

</dl>

Tiago Olivoto <a href='mailto:tiagoolivoto@gmail.com'>tiagoolivoto@gmail.com</a>

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

Description

Usage

Arguments

Author

References

Examples