dim_corr

Takes as input either the Sigma covariance matrix, if the user has learned the factor covariance,
or the Omega covariance matrix, if the user has learned the loading covariance, as well as a vector of
dimension names.
Returns a correlation matrix with correlations between the dimensions.

IRT-M is a semi-supervised approach based on Bayesian Item Response Theory
that produces theoretically identified underlying dimensions from input data
and a constraints matrix. The methodology is fully described in 'Morucci et al. (2024),
"Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models"'.
Details are available at <https://www.cambridge.org/core/journals/american-political-science-review/article/measurement-that-matches-theory-theorydriven-identification-in-item-response-theory-models/395DA1DFE3DCD7B866DC053D7554A30B>.

Margaret Foster

IRTM

Theory-Driven Item Response Theory (IRT) Models

Marco Morucci

David Siegel

dim_corr function

<dl><dt>cov_array</dt>
<dd>An array of dimension (d x d x nsamp/thin) containing posterior samples of the relevant covariance matrix.</dd>
<dt>dim_names</dt>
<dd>Vector of dimension names.</dd></dl>

Arguments

learned correlations — dim_corr

<dl>

<dt>cov_array</dt>
<dd>An array of dimension (d x d x nsamp/thin) containing posterior samples of the relevant covariance matrix.</dd>


<dt>dim_names</dt>
<dd>Vector of dimension names.</dd>

</dl>

dim_corr: learned correlations

Description

Usage

Value

Arguments