get_lambdas

Takes as input the array of lambdas from the irt list,
a vector of item names (can be taken from either Y_in or M_matrix), a vector of dimension names,
and, optionally, a vector comprising elaborations about each item.
Returns a list containing a data frame with the mean lambdas for each item-dimension pair,
possibly attaching elaborations to each item's string,
and a data frame with the items with the highest mean values of lambda for each dimension in order

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

get_lambdas function

<dl><dt>lambda_array</dt>
<dd>An array of dimension (K x d x nsamp/thin) containing posterior samples of item discrimination parameters.</dd>
<dt>item_names</dt>
<dd>Vector of item names.</dd>
<dt>dim_names</dt>
<dd>Vector of dimension names.</dd>
<dt>item_elab</dt>
<dd>A vector comprising elaborations about each item (Default = NULL).</dd></dl>

Arguments

get_lambdas — get_lambdas

<dl>

<dt>lambda_array</dt>
<dd>An array of dimension (K x d x nsamp/thin) containing posterior samples of item discrimination parameters.</dd>


<dt>item_names</dt>
<dd>Vector of item names.</dd>


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


<dt>item_elab</dt>
<dd>A vector comprising elaborations about each item (Default = NULL).</dd>

</dl>

get_lambdas: get_lambdas

Description

Usage

Value

Arguments