PCA_COMP - Principal Component Values

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


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

is the independent variables data matrix, so each column represents one variable.
is the boolean array to select a subset of the input variables in X. If missing, all variables in X are included.
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).
is the component number to return. If missing, the first principal component is assumed.
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.


  1. The underlying model is described here.
  2. 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).
  3. The sample data may include data points with missing values.
  4. Each column in the input matrix corresponds to a separate variable.
  5. Each row in the input matrix corresponds to an observation.
  6. Observations (i.e., rows) with missing values are removed.
  7. The PCA_COMP function is available starting with version 1.60 APACHE.

Files Examples

Related Links



Article is closed for comments.

Was this article helpful?
0 out of 0 found this helpful