bad_coded

10 observation and 3 variables erroneously coded.

datasets

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

bad_coded function

A data set with 10 observations on the following 3 variables:<dl>
<dt><code>V2</code></dt>
<dd>Option 1 is omited</dd><dt><code>V3</code></dt>
<dd>Options go from 1 to 8</dd><dt><code>V4</code></dt>
<dd>Option 1 omited, other are not consecutive and there is NA</dd>
</dl>

Format

Nishisato and Clavel, artificial set of data — bad_coded

A data set with 10 observations on the following 3 variables:<dl>
<dt><code>V2</code></dt>
<dd>Option 1 is omited</dd>

<dt><code>V3</code></dt>
<dd>Options go from 1 to 8</dd>

<dt><code>V4</code></dt>
<dd>Option 1 omited, other are not consecutive and there is NA</dd>


</dl>

bad_coded: Nishisato and Clavel, artificial set of data

Description

Usage

Arguments

Format

Details

References