ItemLift

ItemLift.default

ItemLift.binary

The lift is a commonly used index in a POS data analysis.
The item lift of Item k to Item j is defined as follow:
\( l_{jk} = \frac{p_{k\mid j}}{p_k} \)
This function is applicable only to binary response data.

Implements comprehensive test data engineering methods as described in
Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for
engineering and processing test data: Classical Test Theory (CTT) with
reliability coefficients for continuous ability assessment; Item Response
Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information
functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank
Analysis (LRA) for ordinal clustering with automatic determination of cluster
numbers; Biclustering methods including infinite relational models for
simultaneous clustering of examinees and items without predefined cluster
numbers; and Bayesian Network Models (BNM) for visualizing inter-item
dependencies. Features local dependence analysis through LRA and biclustering,
parameter estimation, dimensionality assessment, and network structure
visualization for educational, psychological, and social science research.

Koji Kosugi 

exametrika

Test Theory Analysis and Biclustering

ItemLift function

<dl><dt>U</dt>
<dd>Either an object of class "exametrika" or raw data. When raw data is given,
it is converted to the exametrika class with the <code>dataFormat</code> function.</dd>
<dt>na</dt>
<dd>Values to be treated as missing values.</dd>
<dt>Z</dt>
<dd>Missing indicator matrix of type matrix or data.frame. Values of 1 indicate
observed responses, while 0 indicates missing data.</dd>
<dt>w</dt>
<dd>Item weight vector specifying the relative importance of each item.</dd></dl>

Arguments

Item Lift — ItemLift

<dl>

<dt>U</dt>
<dd>Either an object of class "exametrika" or raw data. When raw data is given,
it is converted to the exametrika class with the <code>dataFormat</code> function.</dd>


<dt>na</dt>
<dd>Values to be treated as missing values.</dd>


<dt>Z</dt>
<dd>Missing indicator matrix of type matrix or data.frame. Values of 1 indicate
observed responses, while 0 indicates missing data.</dd>


<dt>w</dt>
<dd>Item weight vector specifying the relative importance of each item.</dd>

</dl>

ItemLift: Item Lift

Description

Usage

Value

Arguments

References

Examples