Computes Cross-validated Error Sum of Squares for a Functional Regression Model
fRegress.CV |
Language Reference for FDA Library
|
Computes Cross-validated Error Sum of Squares for a Functional Regression Model
DESCRIPTION:
For a functional regression model with a scalar dependent variable,
a cross-validated error sum of squares is computed. This function
aids the choice of smoothing parameters in this model using the
cross-validated error sum of squares criterion.
USAGE:
fRegress.CV(yvec, xfdlist, betalist)
REQUIRED ARGUMENTS:
- yvec
-
a vector of dependent variable values.
- 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.
VALUE:
the sum of squared errors in predicting
yvec
.
SEE ALSO:
fRegress, fRegress.stderr
EXAMPLES:
See the analyses of the daily weather data.