Fit Fully Functional Linear Model
linmod
Language Reference for FDA Library

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)

REQUIRED ARGUMENTS:

xfdobj
a functional data object for the covariate
a functional data object for the dependent variable
yfdobj

OPTIONAL ARGUMENTS:

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.