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.