Data Analysis using Bootstrap-Coupled ESTimation.
Estimation statistics is a simple framework that avoids the pitfalls of
significance testing. It uses familiar statistical concepts: means,
mean differences, and error bars. More importantly, it focuses on the
effect size of one's experiment/intervention, as opposed to a false
dichotomy engendered by P values.
An estimation plot has two key features:
1. It presents all datapoints as a swarmplot, which orders each point to
display the underlying distribution.
2. It presents the effect size as a bootstrap 95% confidence interval on a
separate but aligned axes.
Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105.
<doi:10.1038/s41592-019-0470-3>.
The free-to-view PDF is located at <https://rdcu.be/bHhJ4>.

Data Analysis using Bootstrap-Coupled Estimation

package-ubuntu-20.04-r-4.0.5#cran#dabestr-0.3.0

<p>Compute the standardizers for Cohen's d
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>x1</code></td>
<td>
<p>A vector of values.</p>
</td>
</tr>
<tr valign="top">
<td><code>x2</code></td>
<td>
<p>Another vector of values.</p>
</td>
</tr>
</table>

<p>Named list for pooled and average standardizers.
Use the pooled one for unpaired Cohens' d, and the average one
for paired Cohens'd d.
</p>

package-ubuntu-20.04-r-4.0.5#cran#dabestr-0.3.0#docs#cohen_d_standardizers

cohen_d_standardizers

Description

Usage

Arguments

Value

dabestr

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