View source: R/smooth.basis.sparse.R
smooth.basis.sparse | R Documentation |
Makes it possible to perform smoothing with smooth.basis
when the
data has NAs.
smooth.basis.sparse(argvals, y, fdParobj, fdnames=NULL, covariates=NULL,
method="chol", dfscale=1 )
argvals |
a set of argument values corresponding to the observations in array
|
y |
an set of values of curves at discrete sampling points or
argument values. If the set is supplied as a matrix object, the rows must
correspond to argument values and columns to replications, and it will be
assumed that there is only one variable per observation. If
|
fdParobj |
a functional parameter object, a functional data object or a
functional basis object. In the simplest case, |
fdnames |
a list of length 3 containing character vectors of names for the following:
|
covariates |
The observed values in |
method |
by default the function uses the usual textbook equations for computing
the coefficients of the basis function expansions. But, as in regression
analysis, a price is paid in terms of rounding error for such
computations since they involved cross-products of basis function
values. Optionally, if |
dfscale |
the generalized cross-validation or "gcv" criterion that is often used
to determine the size of the smoothing parameter involves the
subtraction of an measue of degrees of freedom from |
an object of class fd
.
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
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