PLS Regression Models

PLS Regression; performs partial least squares analyses with a list of continuous dependent variables, and a list of continuous predictor variables.

General

Element Name Description
Detail of computed results reported Specifies the level of computed results reported. If Minimal results is requested, Statistica will report summary statistics for the components and the regression coefficients; if All results is requested, summary plots, weights, and other statistics. Residual (observational) statistics can be requested as a separate option
PLS method Select the algorithm (method) that is to be used to compute the PLS components; the default is PLS. If PLS (the default) is specified, then a standard PLS analysis using the NIPALS algorithm (Rannar, Lindgren, Geladi, and Wold, 1994) will be performed; if you specify SIMPLS the factor scores will be computed via the SIMPLS algorithm (de Jong, 1993).
Max number of components Specifies the maximum number of components to be extracted; the default value is 120.
Delta for R-square; 1.E- Specifies the negative exponent for a base-10 constant delta (delta = 10^-RDelta); the default value is 12. Delta is used by STATISTICA as a criterion for determining whether to stop extracting additional PLS components.
Intercept Specifies whether the intercept (constant) is to be included in the model.
Auto scaling Each column in the predictor design matrix X and matrix of dependent (response) variables Y will be divided by its respective standard deviation, and all computations will be performed on these scaled matrices X and Y. Note that the coefficients that are computed when the AUTOSCALE option is set are not the same as the standardized regression coefficients, as, for example, computed via multiple regression. For more information about auto-scaling, refer to Geladi and Kowalski (1986).
Delta for eigenvalues; 1.E- Specifies the negative exponent for a base-10 constant delta (delta = 10^-Edelta); the default value is 12. Delta is used for checking the convergence of the iterative computation of eigenvectors for each PLS component.
Maximum number of iterations Specifies the maximum number of iterations for the iterative computation of eigenvectors for each PLS component. The default value is 200. PLS uses an iterative power method (see Golub and van Loan, 1996) to compute the eigenvector of Y'XX'Y for each component.

Residuals and Observational Statistics

Element Name Description
Residual analysis Creates predicted and residual values, and factor scores for each observation.
Normal probability plot Normal probability plot of residuals.
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.