Returns an array of cells for the i-th principal component (or residuals).

## Syntax

**PCA_COMP **(**X**, Mask, Standardize, Number, Return)

**X**- is the independent variables data matrix, so each column represents one variable.
**Mask**- is the boolean array to select a subset of the input variables in X. If missing, all variables in X are included.
**Standardize**- is a flag or switch to standardize the input variables before the analysis (i.e., standardize = 1 (default), subtract mean = 2)).
Value Standardize 1 Standardize (subtract mean and divide by standard deviation) ( **default**).2 Subtract mean (subtract mean). **Number**- is the component number to return. If missing, the first principal component is assumed.
**Return**- is a switch to select the return output (1 = proportion of variance (default), 2 = variance, 3 = eigenvalue, 4 = loadings, 5 = PC data).
Value **Return**1 Proportion of total variance ( **default**).2 Variance. 3 Eigenvalue. 4 Loading or weights for input variables. 5 Principal component (PC) data.

## Remarks

- The underlying model is described here.
- The PCA_COMP function must be entered as an array formula (for return-types greater than 3) in a range that has the rows as the number of variables (return-type = 4) or the number of observations (return-type = 5).
- The sample data may include data points with missing values.
- Each column in the input matrix corresponds to a separate variable.
- Each row in the input matrix corresponds to an observation.
- Observations (i.e., rows) with missing values are removed.
- The PCA_COMP function is available starting with version 1.60 APACHE.

## Files Examples

## Related Links

## References

- J. Edward Jackson; A User's Guide to Principal Components; Wiley-Interscience; (Sep 10, 2003), ISBN: 471471348.
- I.T. Jolliffe; Principal Component Analysis; Springer; 2nd Edition(Oct 01, 2002), ISBN: 0387954422.
- John Y. Campbell, Andrew W. Lo, A. Craig MacKinlay, Andrew Y. Lo; The Econometrics of Financial Markets; Princeton University Press; 2nd edition(Dec 09, 1996), ISBN: 691043019.
- Hamilton, J.D.; Time Series Analysis, Princeton University Press (1994), ISBN 0-691-04289-6.
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740.

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