Estimate a smoothing function f(s, t) over a rectangular lattice
smooth.bibasis(sarg, targ, y, fdPars, fdPart, fdnames=NULL, returnMatrix=FALSE)a list with the following components:
a functional data object containing a smooth of the data.
a degrees of freedom measure of the smooth
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.
the coefficient matrix for the basis function expansion of the smoothing function
the error sums of squares. SSE is a vector or a matrix of the same size as GCV.
the penalty matrix.
the matrix mapping the data to the coefficients.
vectors of argument values for the first and second dimensions, respectively, of the surface function.
an array containing surface values measured with noise
functional parameter objects for sarg and targ,
respectively
a list of length 3 containing character vectors of names for
sarg, targ, and the surface function f(s, t).
logical: If TRUE, a two-dimensional is returned using a special class from the Matrix package.
Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.
smooth.basis