matrixpls (version 0.5.0)

optim.GSCA: GSCA optimization criterion

Description

GSCA optimization criterion

Usage

optim.GSCA(matrixpls.res)

Arguments

matrixpls.res

An object of class matrixpls from which the criterion function is calculated

Value

• Sum of residual variances.

Details

Optimization criterion for minimizing the sum of all residual variances in the model.

The matrixpls implementation of the GSCA criterion extends the criterion presented by Huang and Takane by including also the minimization of the residual variances of the formative part of the model. The formative regressions in a model are typically specified to be identical to the weight pattern W.mod resulting zero residual variances by definition. However, it is possible to specify a formative model that does not completely overlap with the weight pattern leading to non-zero residuals that can be optimizes.

References

Hwang, H., & Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69(1), 81–99. doi:10.1007/BF02295841

See Also

Other GSCA functions: inner.GSCA; outer.GSCA

Other Weight optimization criteria: optim.GCCA; optim.maximizeInnerR2; optim.maximizePrediction