ANOVA-PCA
Description
ANOVA-PCA is a combination of both methods developed by Harrington. The data is partitioned into submatrices corresponding to each experimental factor, which are then subjected to PCA separately after adding the residual error back. If the effect of a factor is large compared to the residual error, separation along the 1st PC in the score plot should be evident. With this method, the significance of a factor can be visually determined.
The procedure is in two stages:
- A decomposition of the data matrix into a set of matrices representing factors being part of a design of experiments, and a matrix of residuals.
- A principal component analysis on each of factor matrix added with the residual matrix.
See Harrington P.B. et al. 2005.
Usage
| Field | Description |
|---|---|
| Launch | Field indicating whether you want to execute this analysis in the workflow (checked) or not (unchecked). |
to finish …
Output Examples
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F1 Scores
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F2 Scores
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Interactions
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Residuals
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