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SVD

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

- Subject Column - Set a column to be vectorized by reduced dimensions.
- Key Column - Set a column to use as dimensions.
- Value Column (Optional) - Set a column to use as values. If empty, count of subject and key pair is used.
- Fill (Optional) - The default is 0. This is what should be used for missing value in groups.
- Aggregate Function (Optional) - The default is mean. This is how duplicated data should be aggregated.

Parameters

- Output Type (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.

- Number of Dimensions (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.
- Centralize Data - The default is TRUE. Set if data should be centralized to the origin.
- Output Format (Optional) - The defualt 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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