fda (version 1.2.3)

linmod: Fit Fully Functional Linear Model

Description

A functional dependent variable is approximated by a single functional covariate, and the covariate can affect the dependent variable for all values of its argument. The regression function is a bivariate function.

Usage

linmod(xfdobj, yfdobj, wtvec=rep(1,nrep),
       xLfdobj=int2Lfd(2), yLfdobj=int2Lfd(2),
       xlambda=0, ylambda=0)

Arguments

xfdobj

a functional data object for the covariate

yfdobj

a functional data object for the dependent variable

wtvec

a vector of weights for each observation.

xLfdobj

either a nonnegative integer or a linear differential operator object. This operator is applied to the regression function's first argument.

yLfdobj

either a nonnegative integer or a linear differential operator object. This operator is applied to the regression function's second argument.

xlambda

a smoothing parameter for the first argument of the regression function.

ylambda

a smoothing parameter for the second argument of the regression function.

Value

• a named list of length 3 with the following entries:
• alphafd
  the intercept functional data object.
• regfd
  a bivariate functional data object for the regression function.
• yhatfd
  a functional data object for the approximation to the dependent variable defined by the linear model, if the dependent variable is functional. Otherwise the matrix of approximate values.

See Also

fRegress

Examples

#See the prediction of precipitation using temperature as
#the independent variable in the analysis of the daily weather
#data.