fda (version 2.4.7)

fRegress.CV: Computes Cross-validated Error Sum of Integrated Squared Errors for a Functional Regression Model

Description

For a functional regression model, a cross-validated error sum of squares is computed. For a functional dependent variable this is the sum of integrated squared errors. For a scalar response, this function has been superceded by the OCV and gcv elements returned by fRegress. This function aids the choice of smoothing parameters in this model using the cross-validated error sum of squares criterion.

Usage

#fRegress.CV(y, xfdlist, betalist, wt=NULL, CVobs=1:N,
#            returnMatrix=FALSE, ...)
#NOTE:  The following is required by CRAN rules that
# function names like "as.numeric" must follow the documentation
# standards for S3 generics, even when they are not.
# Please ignore the following line:
# S3 method for CV
fRegress(y, xfdlist, betalist, wt=NULL, CVobs=1:N,
            returnMatrix=FALSE, ...)

Arguments

y

the dependent variable object.

xfdlist

a list whose members are functional parameter objects specifying functional independent variables. Some of these may also be vectors specifying scalar independent variables.

betalist

a list containing functional parameter objects specifying the regression functions and their level of smoothing.

wt

weights for weighted least squares. Defaults to all 1's.

CVobs

Indices of observations to be deleted. Defaults to 1:N.

returnMatrix

logical: If TRUE, a two-dimensional is returned using a special class from the Matrix package.

…

optional arguments not used by fRegress.CV but needed for superficial compatibability with fRegress methods.

Value

A list containing

SSE.CV

The sum of squared errors, or integrated squared errors

errfd.cv

Either a vector or a functional data object giving the cross-validated errors

See Also

fRegress, fRegress.stderr

Examples

# NOT RUN {
#See the analyses of the Canadian daily weather data.
# }