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

mse function

<dl><dt>ytrue</dt>
<dd>observed values,</dd>
<dt>ypred</dt>
<dd>predicted values</dd>
<dt>aggregate</dt>
<dd>logical for whether to take mean of estimate</dd>
<dt>root</dt>
<dd>logical for whether to return square root of MSE</dd></dl>

Arguments

Mean Squared Error — mse

<dl>

<dt>ytrue</dt>
<dd>observed values,</dd>


<dt>ypred</dt>
<dd>predicted values</dd>


<dt>aggregate</dt>
<dd>logical for whether to take mean of estimate</dd>


<dt>root</dt>
<dd>logical for whether to return square root of MSE</dd>

</dl>

mse: Mean Squared Error

Description

Usage

Value

Arguments