Usage
ff(X, yind, xind = seq(0, 1, l = ncol(X)), basistype = c("te", "t2", "s"),
integration = c("simpson", "trapezoidal", "riemann"), L = NULL, limits = NULL,
splinepars = list(bs = "ps", m = list(c(2, 1), c(2, 1))), check.ident = TRUE)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
matrix (or vector) of indices of evaluations
of $Y_i(t)$
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
$\beta(t,s)$. Alternatively, use "s" for
bivariate basis functions (see mgcv's
integration
method used for numerical integration.
Defaults to "simpson"'s rule for calculating
entries in L. Alternatively and for
non-equidistant grids, "trapezoidal" or
"riemann". "riemann"
L
optional: an n by ncol(xind) matrix
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 functio
splinepars
optional arguments supplied to the
basistype-term. Defaults to a cubic tensor product
B-spline with marginal first difference penalties, i.e.
list(bs="ps", m=c(2, 1)) See
te check.ident
check rank of
$Cov(X_i(s),X_i(s'))$ and adjust
number of basis functions if necessary. See Details.
Defaults to TRUE.