ds_mc

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 function

<dl><dt>input</dt>
<dd>A data set with valid data</dd>
<dt>solutions</dt>
<dd>Optional argument. A number of intended solutions</dd>
<dt>mode</dt>
<dd>Optional argument. In case of NA values, the action to be taken.
See help("ds_mc_check") for more information. Radical action by default.</dd></dl>

Arguments

Multiple choice data analysis — ds_mc

<dl>

<dt>input</dt>
<dd>A data set with valid data</dd>


<dt>solutions</dt>
<dd>Optional argument. A number of intended solutions</dd>


<dt>mode</dt>
<dd>Optional argument. In case of NA values, the action to be taken.
See help("ds_mc_check") for more information. Radical action by default.</dd>

</dl>

Multiple choice data analysis

ds_mc: Multiple choice data analysis

Description

Usage

Value

Arguments

See Also

Examples