utils_samples

sample_random

sample_systematic

<ul>
<li><code>sample_random()</code> performs Simple Random Sampling or Stratified Random
Sampling</li>
<li><code>sample_systematic()</code> performs systematic sampling. In this case, a regular
interval of size k (<code>k = floor(N/n)</code>) is generated considering the population
size (N) and desired sample size (n). Then, the starting member (<code>r</code>) is
randomly chosen between <code>1-k</code>. The second element is <code>r</code> + <code>k</code>, and so on.</li>
</ul>

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

utils_samples function

<dl><dt>data</dt>
<dd>A data frame. If <code>data</code> is a <code>grouped_df</code>, the operation will be
performed on each group (stratified).</dd>
<dt>n, prop</dt>
<dd>Provide either <code>n</code>, the number of rows, or <code>prop</code>, the
proportion of rows to select. If neither are supplied, <code>n = 1</code> will be
used.</dd>
<dt>by</dt>
<dd>A categorical variable to compute the sample by. It is a
shortcut to <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">dplyr::group_by()</a></code> that allows to group the data by one
categorical variable. If more than one grouping variable needs to be used,
use <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">dplyr::group_by()</a></code> to pass the data grouped.</dd>
<dt>weight</dt>
<dd>Sampling weights. This must evaluate to a vector of
non-negative numbers the same length as the input. Weights are
automatically standardised to sum to 1.</dd>
<dt>r</dt>
<dd>The starting element. By default, <code>r</code> is randomly selected between
<code>1:k</code></dd></dl>

Arguments

Random Sampling — utils_samples

<dl>

<dt>data</dt>
<dd>A data frame. If <code>data</code> is a <code>grouped_df</code>, the operation will be
performed on each group (stratified).</dd>


<dt>n, prop</dt>
<dd>Provide either <code>n</code>, the number of rows, or <code>prop</code>, the
proportion of rows to select. If neither are supplied, <code>n = 1</code> will be
used.</dd>


<dt>by</dt>
<dd>A categorical variable to compute the sample by. It is a
shortcut to <code><a href='https://dplyr.tidyverse.org/reference/group_by.html'>dplyr::group_by()</a></code> that allows to group the data by one
categorical variable. If more than one grouping variable needs to be used,
use <code><a href='https://dplyr.tidyverse.org/reference/group_by.html'>dplyr::group_by()</a></code> to pass the data grouped.</dd>


<dt>weight</dt>
<dd>Sampling weights. This must evaluate to a vector of
non-negative numbers the same length as the input. Weights are
automatically standardised to sum to 1.</dd>


<dt>r</dt>
<dd>The starting element. By default, <code>r</code> is randomly selected between
<code>1:k</code></dd>

</dl>

utils_samples: Random Sampling

Description

Usage

Value

Arguments

Examples