Defines a term \(\int^{s_{hi, i}}_{s_{lo, i}} X_i(s)\beta(t,s)ds\) for
inclusion in an mgcv::gam-formula (or bam or gamm or
gamm4:::gamm4) as constructed by pffr.
Defaults to a
cubic tensor product B-spline with marginal first order differences penalties
for \(\beta(t,s)\) and numerical integration over the entire range
\([s_{lo, i}, s_{hi, i}] = [\min(s_i), \max(s_i)]\) by using Simpson
weights. Can't deal with any missing \(X(s)\), unequal lengths of
\(X_i(s)\) not (yet?) possible. Unequal integration ranges for different
\(X_i(s)\) should work. \(X_i(s)\) is assumed to be numeric (duh...).
ff(
X,
yind = NULL,
xind = seq(0, 1, l = ncol(X)),
basistype = c("te", "t2", "ti", "s", "tes"),
integration = c("simpson", "trapezoidal", "riemann"),
L = NULL,
limits = NULL,
splinepars = if (basistype != "s") {
list(bs = "ps", m = list(c(2, 1), c(2, 1)), k
= c(5, 5))
} else {
list(bs = "tp", m = NA)
},
check.ident = TRUE
)A list containing
an n by ncol(xind) matrix of function evaluations
\(X_i(s_{i1}),\dots, X_i(s_{iS})\); \(i=1,\dots,n\).
DEPRECATED used to supply matrix (or vector) of indices of evaluations of \(Y_i(t)\), no longer used.
vector of indices of evaluations of \(X_i(s)\), i.e, \((s_{1},\dots,s_{S})\)
defaults to "te", i.e. a tensor product
spline to represent \(\beta(t,s)\). Alternatively, use "s" for
bivariate basis functions (see mgcv's s) or
"t2" for an alternative parameterization of tensor product splines
(see mgcv's t2).
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" integration is always used if limits is specified
optional: an n by ncol(xind) matrix giving the weights for
the numerical integration over \(s\).
defaults to NULL for integration across the entire range of
\(X(s)\), otherwise specifies the integration limits \(s_{hi}(t),
s_{lo}(t)\): either one of "s<t" or "s<=t" for
\((s_{hi}(t), s_{lo}(t)) = (t, 0]\) or \([t, 0]\), respectively, 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.
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=list(c(2, 1), c(2,1))). See
te or s in mgcv for details
check identifiability of the model spec. See Details and
References. Defaults to TRUE.
Fabian Scheipl, Sonja Greven
If check.ident==TRUE and basistype!="s" (the default), the
routine checks conditions for non-identifiability of the effect. This occurs
if a) the marginal basis for the functional covariate is rank-deficient
(typically because the functional covariate has lower rank than the spline
basis along its index) and simultaneously b) the kernel of Cov\((X(s))\) is
not disjunct from the kernel of the marginal penalty over s. In
practice, a) occurs quite frequently, and b) occurs usually because
curve-wise mean centering has removed all constant components from the
functional covariate.
If there is kernel overlap, \(\beta(t,s)\) is
constrained to be orthogonal to functions in that overlap space (e.g., if the
overlap contains constant functions, constraints "\(\int \beta(t,s) ds =
0\) for all t" are enforced). See reference for details.
A warning is
always given if the effective rank of Cov\((X(s))\) (defined as the number
of eigenvalues accounting for at least 0.995 of the total variance in
\(X_i(s)\)) is lower than 4. If \(X_i(s)\) is of very low rank,
ffpc-term may be preferable.
For background on check.ident:
Scheipl, F., Greven,
S. (2016). Identifiability in penalized function-on-function regression
models. Electronic Journal of Statistics, 10(1), 495--526.
https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-10/issue-1/Identifiability-in-penalized-function-on-function-regression-models/10.1214/16-EJS1123.full
mgcv's linear.functional.terms