Computes Cross-validated Error Sum of Integrated Squared Errors for a Functional Regression Model
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has been superseded by the OCV and gcv elements returned by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.
#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 class 'CV'
fRegress(y, xfdlist, betalist, wt=NULL, CVobs=1:N,
returnMatrix=FALSE, ...)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 |
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 the analyses of the Canadian daily weather data.
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