pls (version 2.1-0)

svdpc.fit: Principal Component Regression

Description

Fits a PCR model using the singular value decomposition.

Usage

svdpc.fit(X, Y, ncomp, stripped = FALSE, ...)

Value

• A list containing the following components is returned:
• coefficients
  an array of regression coefficients for 1, ..., ncomp components. The dimensions of coefficients are c(nvar, npred, ncomp) with nvar the number of X variables and npred the number of variables to be predicted in Y.
• scores
  a matrix of scores.
• loadings
  a matrix of loadings.
• Yloadings
  a matrix of Y-loadings.
• projection
  the projection matrix used to convert X to scores.
• Xmeans
  a vector of means of the X variables.
• Ymeans
  a vector of means of the Y variables.
• fitted.values
  an array of fitted values. The dimensions of fitted.values are c(nobj, npred, ncomp) with nobj the number samples and npred the number of Y variables.
• residuals
  an array of regression residuals. It has the same dimensions as fitted.values.
• Xvar
  a vector with the amount of X-variance explained by each number of components.
• Xtotvar
  Total variance in X.
• If stripped is TRUE, only the components coefficients, Xmeans and Ymeans are returned.

encoding

latin1

Details

This function should not be called directly, but through the generic functions pcr or mvr with the argument method="svdpc". The singular value decomposition is used to calculate the principal components.

References

Martens, H., N�s, T. (1989) Multivariate calibration. Chichester: Wiley.

See Also

mvr plsr pcr