plot_cfa_k

Displays the item-level estimates of a <code>cfa_k</code> fit, one
panel per factor, with each estimate's confidence interval. The
display is built to make the equality questions behind the classical
measurement structures visible: a dashed vertical line marks, per
factor, either the common (equated) estimate when the plotted
parameter was constrained equal, or the mean of the free estimates as
an informal anchor for the question "could these plausibly be one
value?". Confidence intervals that all cover the anchor are what
equal loadings (or equal error variances, or equal intercepts) would
look like; an interval far from it shows which item resists the
constraint, and the likelihood ratio test of the two nested
<code>cfa_k()</code> fits is the formal companion (see the examples in
<code>cfa_k</code>).

hplot

Methods for design, measurement, and analysis, with the
aim of being user friendly yet methodologically sound. 'DMAR' (pronounced "Dee-Mar") implements
many advanced and nonstandard methods and makes them available for
straightforward use, with interfaces, defaults, and documentation
that are consistent across the package and grounded in the
methodological literature, in support of sound and reproducible
results. The package
emphasizes effect size estimation with confidence intervals;
sample size planning through accuracy in parameter estimation
(AIPE) and power analysis (including composite power for designs
whose conclusions require several results to hold at once), with
minimum risk, sequential, and equivalence frameworks; reliability,
agreement, and measurement more broadly, from coefficient omega
with confidence intervals to measurement invariance; factor
analysis and structural equation modeling, in which constructs,
latent variables measured by multiple indicators, are modeled
directly, with confirmatory factor analysis, convergent and
discriminant validity, and sample size planning for structural
equation models; mediation analysis, from the
simple mediation model with bootstrap intervals to likelihood
ratio tests of arbitrary indirect effects by model-based
constrained optimization (MBCO), with multiple groups and the probing of
moderated mediation; equivalence and noninferiority testing;
meta-analysis; repeated measures, multivariate, ANOVA, and ANCOVA
designs; and inference grounded in model comparison throughout.
Measurement is approached from a psychometric perspective, and
although many of the methods grew up in human-centered research,
they apply broadly across the empirical sciences.
Much of what is implemented traces to the author's methodological
work, interests, and collaborations. 'DMAR' is a more modern, more general,
and greatly expanded reimagining of the 'MBESS' package (Kelley,
2007a, <doi:10.18637/jss.v020.i08>; 2007b,
<doi:10.3758/BF03192993>), which has been on CRAN for more than
two decades and remains available there in stable form. Most functions accept either
raw data or the summary statistics typically reported in published
articles, so an analysis can be reproduced from a paper without
the original data, which is useful both for extending a published
analysis and for meta-analytic work. The estimation, inference,
and planning functions return one consistently formatted data
frame per function that composes with the broader R ecosystem, and
confidence intervals are reported alongside effect sizes
throughout, as best practice recommends. Researchers who have data
and a question but who are not R experts will find the package
approachable, while methodologists gain access to advanced and
nonstandard methods, including tables of critical values not
available elsewhere.

Ken Kelley

DMAR

Design, Measurement, and Analysis in R (DMAR)

plot_cfa_k function

<dl><dt>x</dt>
<dd>A <code>dmar_cfa_k</code> object from <code>cfa_k</code> with the
default <code>output = "verbose"</code>.</dd>
<dt>what</dt>
<dd>Which parameter to display: <code>"loadings"</code> (default,
the <code>lambda</code> terms), <code>"errors"</code> (the <code>psi</code> terms), or
<code>"intercepts"</code> (the <code>nu</code> terms; requires a fit with the
mean structure).</dd>
<dt>show_equal_reference</dt>
<dd>Logical. If <code>TRUE</code> (default), draw
the dashed per-factor reference line described above. When the
parameter was constrained equal the line is the common estimate and
is always drawn.</dd>
<dt>xlab</dt>
<dd>Label for the horizontal axis. Defaults to a description
of the plotted parameter.</dd>
<dt>title</dt>
<dd>Optional plot title.</dd>
<dt>palette</dt>
<dd>Character string naming the color palette. Defaults to
<code>"okabe_ito"</code>, base R's colorblind-safe Okabe-Ito palette;
<code>"tableau"</code> is also available.</dd></dl>

Arguments

Ken Kelley <a href="/link/kkelley%40nd.edu?package=DMAR&version=1.0.0" data-mini-rdoc="DMAR::kkelley@nd.edu">kkelley@nd.edu</a>

Author

Plot the Estimates of a Multiple-Factor CFA — plot_cfa_k

<dl>

<dt>x</dt>
<dd>A <code>dmar_cfa_k</code> object from <code>cfa_k</code> with the
default <code>output = "verbose"</code>.</dd>


<dt>what</dt>
<dd>Which parameter to display: <code>"loadings"</code> (default,
the <code>lambda</code> terms), <code>"errors"</code> (the <code>psi</code> terms), or
<code>"intercepts"</code> (the <code>nu</code> terms; requires a fit with the
mean structure).</dd>


<dt>show_equal_reference</dt>
<dd>Logical. If <code>TRUE</code> (default), draw
the dashed per-factor reference line described above. When the
parameter was constrained equal the line is the common estimate and
is always drawn.</dd>


<dt>xlab</dt>
<dd>Label for the horizontal axis. Defaults to a description
of the plotted parameter.</dd>


<dt>title</dt>
<dd>Optional plot title.</dd>


<dt>palette</dt>
<dd>Character string naming the color palette. Defaults to
<code>"okabe_ito"</code>, base R's colorblind-safe Okabe-Ito palette;
<code>"tableau"</code> is also available.</dd>

</dl>

Ken Kelley <a href='mailto:kkelley@nd.edu'>kkelley@nd.edu</a>

plot_cfa_k: Plot the Estimates of a Multiple-Factor CFA

Description

Usage

Value

Arguments

Author

See Also

Examples