Usage
af_old(X, argvals = seq(0, 1, l = ncol(X)), xind = NULL,
basistype = c("te", "t2", "s"), integration = c("simpson", "trapezoidal",
"riemann"), L = NULL, splinepars = list(bs = "ps", k =
c(min(ceiling(nrow(X)/5), 20), min(ceiling(ncol(X)/5), 20)), m = list(c(2, 2),
c(2, 2))), presmooth = TRUE, Xrange = range(X), Qtransform = FALSE)Arguments
X
an N by J=ncol(argvals) matrix of function evaluations
$X_i(t_{i1}),., X_i(t_{iJ}); i=1,.,N.$
argvals
matrix (or vector) of indices of evaluations of $X_i(t)$; i.e. a matrix with
ith row $(t_{i1},.,t_{iJ})$
xind
Same as argvals. It will discard this argument in the next version of refund.
basistype
defaults to "te", i.e. a tensor product spline to represent $F(x,t)$ Alternatively,
use "s" for bivariate basis functions (see s) or "t2" for an alternative
parameterizati 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" integra
L
optional weight matrix for the linear functional
splinepars
optional arguments specifying options for representing and penalizing the
function $F(x,t)$. Defaults to a cubic tensor product B-spline with marginal second-order
difference penalties, i.e. list(bs="ps", m=list(c(2, 2), c(2, 2)), see
presmooth
logical; if true, the functional predictor is pre-smoothed prior to fitting; see
smooth.basisPar Xrange
numeric; range to use when specifying the marginal basis for the x-axis. It may
be desired to increase this slightly over the default of range(X) if concerned about predicting
for future observed curves that take values outside of
Qtransform
logical; should the functional be transformed using the empirical cdf and
applying a quantile transformation on each column of X prior to fitting? This ensures
Xrange=c(0,1). If Qtransform=TRUE and presmoot