check_sphericity_bartlett

Bartlett's (1951) test of sphericity tests whether a matrix (of correlations)
is significantly different from an identity matrix. The test provides
probability that the correlation matrix has significant correlations among at
least some of the variables in a dataset, a prerequisite for factor analysis
to work. In other words, before starting with factor analysis, one needs to
check whether Bartlett’s test of sphericity is significant.

Utilities for computing measures to assess model quality,
which are not directly provided by R's 'base' or 'stats' packages.
These include e.g. measures like r-squared, intraclass correlation
coefficient (Nakagawa, Johnson & Schielzeth (2017)
<doi:10.1098/rsif.2017.0213>), root mean squared error or functions to
check models for overdispersion, singularity or zero-inflation and
more. Functions apply to a large variety of regression models,
including generalized linear models, mixed effects models and Bayesian
models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.

Daniel Lüdecke

performance

Assessment of Regression Models Performance

Dominique Makowski

Mattan S. Ben-Shachar

Indrajeet Patil

Philip Waggoner

Brenton M. Wiernik

Vincent Arel-Bundock

Rémi Thériault

Martin Jullum

gjo11 

check_sphericity_bartlett function

<dl><dt>x</dt>
<dd>A dataframe.</dd>
<dt>...</dt>
<dd>Arguments passed to or from other methods.</dd></dl>

Arguments

Bartlett's Test of Sphericity — check_sphericity_bartlett

<dl>

<dt>x</dt>
<dd>A dataframe.</dd>


<dt>...</dt>
<dd>Arguments passed to or from other methods.</dd>

</dl>

Bartlett's Test of Sphericity

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check_sphericity_bartlett: Bartlett's Test of Sphericity

Description

Usage

Value

Arguments

Details

References

Examples