Usage
sff(X, yind, xind = seq(0, 1, l = ncol(X)), basistype = c("te", "t2", "s"),
integration = c("simpson", "trapezoidal"), L = NULL, limits = NULL,
splinepars = list(bs = "ps", m = c(2, 2, 2)))Arguments
X
an n by ncol(xind) matrix of function evaluations
$X_i(s_{i1}),\dots, X_i(s_{iS})$; $i=1,\dots,n$.
yind
DEPRECATED matrix (or vector) of indices of evaluations of
$Y_i(t)$; i.e. matrix with rows $(t_{i1},\dots,t_{iT})$; no longer
used.
xind
matrix (or vector) of indices of evaluations of $X_i(s)$;
i.e. matrix with rows $(s_{i1},\dots,s_{iS})$
basistype
defaults to "te", i.e. a tensor product
spline to represent $f(X_i(s), t)$. Alternatively, use "s" for
bivariate basis functions (see s) or " integration
method used for numerical integration. Defaults to
"simpson"'s rule. Alternatively and for non-equidistant grids,
"trapezoidal".
L
optional: an n by ncol(xind) giving the weights for the
numerical integration over $s$.
limits
defaults to NULL for integration across the entire range of
$X(s)$, otherwise specifies the integration limits $s_{hi, i},
s_{lo, i}$: either one of "s or "s<=t"< code=""> for $(s_{hi,
i}, s_{lo, i}) = (0, t)$ or a function that takes <
splinepars
optional arguments supplied to the basistype-term.
Defaults to a cubic tensor product B-spline with marginal second
differences, i.e. list(bs="ps", m=c(2,2,2)). See
te or