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smooth.bibasis

Smooth a discrete surface over a rectangular lattice


Description

Estimate a smoothing function f(s, t) over a rectangular lattice

Usage

smooth.bibasis(sarg, targ, y, fdPars, fdPart, fdnames=NULL, returnMatrix=FALSE)

Arguments

sarg, targ

vectors of argument values for the first and second dimensions, respectively, of the surface function.

y

an array containing surface values measured with noise

fdPars, fdPart

functional parameter objects for sarg and targ, respectively

fdnames

a list of length 3 containing character vectors of names for sarg, targ, and the surface function f(s, t).

returnMatrix

logical: If TRUE, a two-dimensional is returned using a special class from the Matrix package.

Value

a list with the following components:

fdobj

a functional data object containing a smooth of the data.

df

a degrees of freedom measure of the smooth

gcv

the value of the generalized cross-validation or GCV criterion. If the function is univariate, GCV is a vector containing the error sum of squares for each function, and if the function is multivariate, GCV is a NVAR by NCURVES matrix.

coef

the coefficient matrix for the basis function expansion of the smoothing function

SSE

the error sums of squares. SSE is a vector or a matrix of the same size as GCV.

penmat

the penalty matrix.

y2cMap

the matrix mapping the data to the coefficients.

See Also


fda

Functional Data Analysis

v5.1.9
GPL (>= 2)
Authors
J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], Spencer Graves <spencer.graves@effectivedefense.org> [ctb], Giles Hooker <gjh27@cornell.edu> [ctb]
Initial release
2020-12-16

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