ds_mc_check

Function to identify incorrect Multiple Choice input data

Dual Scaling, developed by Professor Shizuhiko Nishisato (1994, ISBN: 0-9691785-3-6), is a fundamental technique in multivariate analysis used for data scaling and correspondence analysis. Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets.

Jose G Clavel

dualScale

Dual Scaling Analysis of Data

Jose G. Clavel

Shizuiko Nishisato

Roberto de la Banda

Antonio Pita

ds_mc_check function

<dl><dt>input</dt>
<dd>The input data to be checked</dd>
<dt>mode</dt>
<dd>Do you want to use a radical ("rad") correction mode
or active ("act") allocations?</dd></dl>

Arguments

Function to identify incorrect Multiple Choice input data — ds_mc_check

<dl>

<dt>input</dt>
<dd>The input data to be checked</dd>


<dt>mode</dt>
<dd>Do you want to use a radical ("rad") correction mode
or active ("act") allocations?</dd>

</dl>

ds_mc_check: Function to identify incorrect Multiple Choice input data

Description

Usage

Value

Arguments

Examples