Gets the regression coefficients matrix, B, for the PLS2 calculation. B satisifies the relationship

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ResponseVector = XB + E

where X and ResponseVector are, respectively, the centered independent and dependent variables values, and E is a noise term for the model.

Gets and sets the default value for the maximum number of iterations to be performed when use the power method for computing dominant eigenvectors and eigenvalues needed by the SIMPLS algorithm.

Gets and sets the default value for the tolerance used to determine convergence of the power method for computing dominant eigenvectors and eigenvalues needed by the SIMPLS algorithm.

Whether the most recent calculation was successful.

Gets and sets the maximum number of iterations to be performed when using the iterative power method to find dominant eigenvectors.

Gets any message that may have been generated by the algorithm. For example, if the calculation is unsuccessful, the message indicates the reason.

Gets the matrix of orthogonal loadings, the basis for the predictor loadings matrix.

Gets the matrix of predictor loadings. The matrix of predictor loadings, P, is defined by

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P = X'T

where X' is the transpose of the predictor matrix and T is the predictor score matrix.

Gets the vector of means for the predictor variables.

Gets the matrix of predictor scores.

Gets the matrix of predictor weights.

Gets the matrix of response loadings. The matrix of response loadings, Q, is defined by

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Q = Y'T

where Y' is the transpose of the response matrix and T is the predictor score matrix.

Gets the vector of means for the response variables.

Gets the matrix of response scores.

Gets and sets the tolerance to be used in the iterative power method that is used to compute dominant eigenvectors. The power method converges if changes in the normalized eigenvector, with respect to the infinity norm, is less than this specified tolerance.