grubbs_outliers

Detects one or more outliers in a numeric variable using the iterative Grubbs' test,
which assumes the data follow a normal distribution.

Provides a comprehensive set of tools for descriptive statistics,
graphical data exploration, outlier detection, homoscedasticity testing, and
multiple comparison procedures. Includes manual implementations of Levene's test,
Bartlett's test, and the Fligner-Killeen test, as well as post hoc comparison
methods such as Tukey, Scheffé, Games-Howell, Brunner-Munzel, and others.
This version introduces two new procedures: the Jonckheere-Terpstra trend test
and the Jarque-Bera test with Glinskiy's (2024) correction. Designed for use in
teaching, applied statistical analysis, and reproducible research.
Additionally you can find a post hoc Test Planner, which helps you to make a
decision on which procedure is most suitable.

Carlos Jiménez-Gallardo

Analitica

Exploratory Data Analysis, Group Comparison Tools, and Other
Procedures

grubbs_outliers function

<dl><dt>dataSet</dt>
<dd>A <code>data.frame</code> containing the data.</dd>
<dt>vD</dt>
<dd>Unquoted name of the numeric variable to be tested for outliers.</dd>
<dt>alpha</dt>
<dd>Significance level for the test (default is <code>0.05</code>).</dd></dl>

Arguments

Outlier Detection Using Grubbs' Test (Iterative) — grubbs_outliers

<dl>

<dt>dataSet</dt>
<dd>A <code>data.frame</code> containing the data.</dd>


<dt>vD</dt>
<dd>Unquoted name of the numeric variable to be tested for outliers.</dd>


<dt>alpha</dt>
<dd>Significance level for the test (default is <code>0.05</code>).</dd>

</dl>

grubbs_outliers: Outlier Detection Using Grubbs' Test (Iterative)

Description

Usage

Value

Arguments

Details

References

Examples