linmod {fda} | R Documentation |
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.
[Package
fda version 2.1.1
Index]