Defines a term \(\int^{s_{hi, i}}_{s_{lo, i}} f(X_i(s), s, t) ds\) for
inclusion in an `mgcv::gam`

-formula (or `bam`

or `gamm`

or
`gamm4:::gamm`

) as constructed by `pffr`

. Defaults to a
cubic tensor product B-spline with marginal second differences penalties for
\(f(X_i(s), s, t)\) and integration over the entire range \([s_{lo, i},
s_{hi, i}] = [\min(s_i), \max(s_i)]\). Can't deal with any missing \(X(s)\),
unequal lengths of \(X_i(s)\) not (yet?) possible. Unequal ranges for
different \(X_i(s)\) should work. \(X_i(s)\) is assumed to be numeric.
`sff()`

IS AN EXPERIMENTAL FEATURE AND NOT WELL TESTED YET -- USE AT
YOUR OWN RISK.

```
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))
)
```

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

vector of indices of evaluations of \(X_i(s)\), i.e, \((s_{1},\dots,s_{S})\)

basistype

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<t"`

or `"s<=t"`

for \((s_{hi,
i}, s_{lo, i}) = (0, t)\) or a function that takes `s`

as the first and
`t`

as the second argument and returns TRUE for combinations of values
`(s,t)`

if `s`

falls into the integration range for the given
`t`

. This is an experimental feature and not well tested yet; use at
your own risk.

a list containing

`call`

a "call" to`te`

(or`s`

,`t2`

) using the appropriately constructed covariate and weight matrices (see`linear.functional.terms`

)`data`

a list containing the necessary covariate and weight matrices