Exploratory

Search…

Multi Dimensional Scaling

Introduction

Calculates coordinations by reducing dimensionality using SVD (Singular Value Decomposition).

How to Access?

You can access from 'Add' (Plus) button.

How to Use?

Column Selection

- Name1 Column - Set one of name columns to calculate coordinations for.
- Name2 Column - Set another column of names to calculate coordinations for.
- Value Column (Optional) - Set a column to use as a distance value. If you use do_dist in the previous step, this will be value.

Parameters

- Number of Dimensions (Optional) - The default is "group".
- "group" is to see how groups are projected to a new coordinations. This corresponds to u matrix of svd.
- "dimension" is to see what direction the new coordinations face. This corresponds to v matrix of svd.
- "variance" is to see how data is scattered in each of the new coordinations. This corresponds to d vector of svd. itself.
- n_component (Optional) - The default is 3. Set number of dimensions to reduce to. This must be less than the number of subjects and the number of keys.

- Number of Dimensions (Optional) - The default is TRUE. Set if data should be centralized to the origin.
- Centralize Data - Whether data should be centralized or not.
- Output Format (Optional) - The default is "wide".
- "wide" - The components will be in columns.
- "long" - The component will be in row.

Take a look at the reference document for the 'svd' function from base R for more details on the parameters.

Last modified 11mo ago

Copy link

On this page

Introduction

How to Access?

How to Use?

Parameters