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
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 <