Functional parameter objects are used as arguments to functions that
estimate functional parameters, such as smoothing functions like
smooth.basis. A bivariate functional parameter object supplies
the analogous information required for smoothing bivariate data using
a bivariate functional data object $x(s,t)$. The arguments are the same as
those for fdPar objects, except that two linear differential
operator objects and two smoothing parameters must be applied,
each pair corresponding to one of the arguments $s$ and $t$ of the
bivariate functional data object.
bifdPar(bifdobj, Lfdobjs=int2Lfd(2), Lfdobjt=int2Lfd(2), lambdas=0, lambdat=0,
estimate=TRUE)a bivariate functional parameter object (i.e., an object of class bifdPar),
which is a list with the following components:
a functional data object (i.e., with class bifd)
a linear differential operator object (i.e., with class
Lfdobjs)
a linear differential operator object (i.e., with class
Lfdobjt)
a nonnegative real number
a nonnegative real number
not currently used
a bivariate functional data object.
either a nonnegative integer or a linear differential operator object for the first argument $s$.
If NULL, Lfdobjs depends on bifdobj[['sbasis']][['type']]:
bspline Lfdobjs <- int2Lfd(max(0, norder-2)), where norder = norder(bifdobj[['sbasis']]).
fourier
Lfdobjs = a harmonic acceleration operator: Lfdobj <- vec2Lfd(c(0,(2*pi/diff(rngs))^2,0), rngs) where rngs = bifdobj[['sbasis']][['rangeval']].
anything elseLfdobj <- int2Lfd(0)
either a nonnegative integer or a linear differential operator object for the first argument $t$.
If NULL, Lfdobjt depends on bifdobj[['tbasis']][['type']]:
bspline Lfdobj <- int2Lfd(max(0, norder-2)), where norder = norder(bifdobj[['tbasis']]).
fourier
Lfdobj = a harmonic acceleration operator: Lfdobj <- vec2Lfd(c(0,(2*pi/diff(rngt))^2,0), rngt) where rngt = bifdobj[['tbasis']][['rangeval']].
anything elseLfdobj <- int2Lfd(0)
a nonnegative real number specifying the amount of smoothing to be applied to the estimated functional parameter $x(s,t)$ as a function of $s$..
a nonnegative real number specifying the amount of smoothing to be applied to the estimated functional parameter $x(s,t)$ as a function of $t$..
not currently used.
linmod
#See the prediction of precipitation using temperature as
#the independent variable in the analysis of the daily weather
#data, and the analysis of the Swedish mortality data.
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