fda.usc (version 0.9.4)

fregre.basis.cv: Cross-validation Functional Regression with scalar response using basis representation.

Description

Computes functional regression between functional explanatory variables and scalar response using basis representation by cross-validation method.

Usage

fregre.basis.cv(fdataobj,y,basis.x=NULL,basis.b=NULL,
type.basis=NULL,lambda=0,Lfdobj=vec2Lfd(c(0,0),rtt),
type.CV=GCV.S,par.CV=list(trim=0),...)

Arguments

fdataobj

fdata class object.

y

Scalar response with length n.

basis.x

Basis for functional explanatory data fdataobj.

basis.b

Basis for functional beta parameter.

type.basis

A vector of character string which determines type of basis. By default "bspline". It is only used when basis.x or basis.b are a vector of number of basis considered.

lambda

A roughness penalty. By default, no penalty lambda=0.

Lfdobj

See eval.penalty.

type.CV

Type of cross-validation. By default generalized cross-validation GCV.S method.

par.CV

List of parameters for type.CV: trim, the alpha of the trimming and draw.

...

Further arguments passed to or from other methods.

Value

• Return:
• call
  The matched call.
• beta.est
  beta coefficient estimated of class fd
• a.est
  Intercept parameter estimated
• fitted.values
  Estimated scalar response.
• H
  Hat matrix.
• residuals
  y minus fitted values.
• df
  The residual degrees of freedom.
• r2
  Coefficient of determination.
• sr2
  Residual variance.
• y
  Scalar response.
• fdataobj
  Functional explanatory data of class fdata.
• lambda.opt
  lambda value that minimizes CV or GCV method.
• gcv.opt
  Minimum value of CV or GCV method. If lambda=0, gcv.opt=NA.
• basis.x.opt
  Basis used for functional explanatory data estimation fdata.
• basis.b
  Basis used for for functional beta parameter estimation.
• lm
  Return lm object.

Details

If basis = NULL creates bspline basis. If the functional covariate fdataobj is in a format raw data, such as matrix or data.frame, creates an object of class fdata with default attributes, see fdata.

References

Ramsay, James O. and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.

See Also

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

Examples

data(tecator)
x<-tecator$absorp.fdata
y=tecator$y$Fat
tt=x[["argvals"]]
a=seq(21,77,by=8)
b=seq(5,17,by=4)
l=2^(-4:15)
res1=fregre.basis.cv(x,y)
summary(res1)
res2=fregre.basis.cv(x,y,a,b,lambda=l,type.CV=GCV.S,par.CV=list(trim=0.05))
ind=c(7,8,9,13,14)
rbind(c(res1[ind],res1$basis.x.opt$type,res1$basis.x.opt$nbasis,
res1$basis.x.opt$type,res1$basis.b.opt$nbasis),
c(res2[ind],res2$basis.x.opt$type,res2$basis.x.opt$nbasis,res2$basis.x.opt$type
,res2$basis.b.opt$nbasis))