explain

<code>explain()</code> provides a human-readable, exhaustive, description of
the results. It also provides references to the key results.

Analyses of frequencies can be performed using an alternative test based
on the G statistic. The test has similar type-I error rates and power as
the chi-square test. However, it is based on a total statistic that can be
decomposed in an additive fashion into interaction effects, main effects, simple
effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call
this set of tools 'ANOFA' (Analysis of Frequency data) to highlight its
similarities with ANOVA. This framework also renders plots of frequencies
along with confidence intervals. Finally, effect sizes and planning statistical
power are easily done under this framework. The ANOFA is a tool that assesses
the significance of effects instead of the significance of parameters; as such,
it is more intuitive to most researchers than alternative approaches based on
generalized linear models.
See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.

Denis Cousineau

ANOFA

Analyses of Frequency Data

Louis Laurencelle

Pier-Olivier Caron

explain function

<dl><dt>object</dt>
<dd>an object to explain</dd>
<dt>...</dt>
<dd>ignored</dd></dl>

Arguments

explain — explain

<dl>

<dt>object</dt>
<dd>an object to explain</dd>


<dt>...</dt>
<dd>ignored</dd>

</dl>

explain: explain

Description

Usage

Value

Arguments