fRegress.CV {fda}R Documentation

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)

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.

[Package fda version 1.1.2 Index]