PLSRfit function

Partial Least Squares Regression (PLSR)

Fits a Partial Least Squares Regression (PLSR) to two continuous data matrices

PLSRfit(Y, X, S = 2, tolerance = 5e-06,
maxiter = 100, show = FALSE)

Arguments

• Y: The matrix of dependent variables
• X: The Matrix of Independent Variables
• S: Dimension of the solution. The default is 2
• tolerance: Tolerance for the algorithm.
• maxiter: Maximum number of iterations for the algorithm.
• show: Logical. Should the calculation process be shown on the screen

Details

Fits a Partial Least Squares Regression (PLSR) to a set of two continuous data matrices

Returns

An object of class "PLSR" - Method: PLSR1

• X: Independent Variables

• Y: Dependent Variables

• center: Are data centered?

• scale: Are data scaled?

• ScaledX: Scaled Independent Variables

• ScaledY: Scaled Dependent Variables

• XScores: Scores for the Independent Variables

• XWeights: Weights for the Independent Variables - coefficients of the linear combination

• XLoadings: Factor loadings for the Independent Variables

• YScores: Scores for the Dependent Variables

• YWeights: Weights for the Dependent Variables - coefficients of the linear combination

• YLoadings: Factor loadings for the Dependent Variables

• XStructure: Structure Correlations for the Independent Variables

• YStructure: Structure Correlations for the Dependent Variables

• YXStructure: Structure Correlations two groups

References

Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2), 109-130.

Author(s)

Jose Luis Vicente Villardon