Multi Dimensional Scaling


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.


  • 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
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