fda.usc (version 0.9.4)

fregre.pls: Functional PLS regression with scalar response

Description

Compute partial least squares regression (PLSR) for functional data.

Usage

fregre.pls(fdataobj,y,l=1:3,...)

Arguments

fdataobj

fdata class object.

l

Index of components to include in the model.

y

Scalar response with length n.

...

Further arguments passed to or from other methods.

Value

• Return:
• call
  The matched call of fregre.pls function.
• beta.est
  Beta coefficient estimated of class fdata.
• coefficients
  A named vector of coefficients.
• fitted.values
  Estimated scalar response.
• coefficents
  A named vector of coefficients.
• residuals
  y-fitted values.
• df
  The residual degrees of freedom.
• r2
  Coefficient of determination.
• sr2
  Residual variance.
• H
  Hat matrix.
• fdataobj
  Functional explanatory data.
• y
  Scalar response.
• l
  Index of components to include in the model.
• pls.fdata
  Fitted object in pls.fdata function.
• lm
  Fitted object in lm function

Details

The partial least squares are calculated by plsr function.

References

Preda C. and Saporta G. PLS regression on a stochastic process. Comput. Statist. Data Anal. 48 (2005): 149{-}158.

See Also

See Also as: fregre.pls.cv, summary.fregre.fd and predict.fregre.fd. Alternative method: fregre.pc, fregre.basis and fregre.np.

Examples

data(tecator)
x<-tecator$absorp.fdata
y<-tecator$y$Fat
res=fregre.pls(x,y,c(1:8))
summary(res)