cv_items

Splits items into k folds, fits on training items, evaluates on held-out items.

Provides tools to adjust estimates of learning for guessing-related
bias in educational and survey research. Implements standard guessing
correction methods and latent class models that leverage informative
pre-post test transitions to account for guessing behavior.
The package helps researchers obtain more accurate estimates of actual
learning when respondents may guess on closed-ended knowledge items.
For theoretical background and empirical validation, see Cor and Sood (2016)
<https://gsood.com/research/papers/guess.pdf>.

Gaurav Sood

guess

Adjust Estimates of Learning for Guessing

Ken Cor

cv_items function

<dl><dt>transmatrix</dt>
<dd>numeric matrix from multi_transmat()</dd>
<dt>k</dt>
<dd>integer number of folds</dd>
<dt>priors</dt>
<dd>optional numeric vector of starting parameters</dd>
<dt>seed</dt>
<dd>optional integer random seed</dd></dl>

Arguments

K-fold cross-validation over items — cv_items

<dl>

<dt>transmatrix</dt>
<dd>numeric matrix from multi_transmat()</dd>


<dt>k</dt>
<dd>integer number of folds</dd>


<dt>priors</dt>
<dd>optional numeric vector of starting parameters</dd>


<dt>seed</dt>
<dd>optional integer random seed</dd>

</dl>

cv_items: K-fold cross-validation over items

Description

Usage

Value

Arguments