Defines a term \(\int_{T}\beta(t)X_i(t)dt\) for inclusion in an [mgcv]{gam}-formula
(or bam or gamm or [gamm4]{gamm4}) as constructed by
fgam, where \(\beta(t)\) is an unknown coefficient function and \(X_i(t)\)
is a functional predictor on the closed interval \(T\). Defaults to a cubic B-spline with
second-order difference penalties for estimating \(\beta(t)\). The functional predictor must
be fully observed on a regular grid.
lf_old(
X,
argvals = seq(0, 1, l = ncol(X)),
xind = NULL,
integration = c("simpson", "trapezoidal", "riemann"),
L = NULL,
splinepars = list(bs = "ps", k = min(ceiling(n/4), 40), m = c(2, 2)),
presmooth = TRUE
)a list with the following entries
call - a call to te (or s, t2) using the appropriately
constructed covariate and weight matrices
argvals - the argvals argument supplied to lf
L - the matrix of weights used for the integration
xindname - the name used for the functional predictor variable in the formula
used by mgcv
tindname - the name used for argvals variable in the formula used by mgcv
LXname - the name used for the L variable in the formula used by mgcv
presmooth - the presmooth argument supplied to lf
Xfd - an fd object from presmoothing the functional predictors using
{smooth.basisPar}. Only present if presmooth=TRUE. See {fd}
an N by J=ncol(argvals) matrix of function evaluations
\(X_i(t_{i1}),., X_i(t_{iJ}); i=1,.,N.\)
matrix (or vector) of indices of evaluations of \(X_i(t)\); i.e. a matrix with ith row \((t_{i1},.,t_{iJ})\)
same as argvals. It will not be supported in the next version of refund.
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
L is specified
an optional N by ncol(argvals) matrix giving the weights for the numerical
integration over t
optional arguments specifying options for representing and penalizing the
functional coefficient \(\beta(t)\). Defaults to a cubic B-spline with second-order difference
penalties, i.e. list(bs="ps", m=c(2, 1)) See te or s for details
logical; if true, the functional predictor is pre-smoothed prior to fitting. See
smooth.basisPar
Mathew W. McLean mathew.w.mclean@gmail.com and Fabian Scheipl
{fgam}, {af}, mgcv's {linear.functional.terms},
{fgam} for examples