| PLSRfit {MultBiplotR} | R Documentation |
Partial Least Squares Regression (PLSR)
Description
Fits a Partial Least Squares Regression (PLSR) to two continuous data matrices
Usage
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
Value
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 |
Author(s)
Jose Luis Vicente Villardon
References
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2), 109-130.