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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 6mo ago

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