PCA_VAR - Calculations using a subset of Principal Components

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


PCA_VAR (X, Mask, Standardize, Number, Number of PC, 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 input variable number.
Number of PC
is the number of principal components (PC) to include. If missing or zero, all components will be used.
is a switch to select the return output (1 = Final communality (default), 2 = Loading/weights, 3 = Fitted values, 4 = Residuals).
Value Return
1 Final communality (default).
2 Loading or weights for factors.
3 Fitted input variable (from PCs).
4 Residuals.


  1. The underlying model is described here.
  2. The PCA_VAR function must be entered as an array formula (for return-types other than 1) in a range that has the rows as the number of variables (return-type = 2) or the number of observations (return-type > 2).
  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 PC_VAR function is available starting with version 1.60 APACHE.

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