lf_old: Construct an FLM regression term

Usage

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)

Arguments

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 not be supported in the next version of refund.

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"

an optional N by ncol(argvals) matrix giving the weights for the numerical integration over t

splinepars

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

presmooth

logical; if true, the functional predictor is pre-smoothed prior to fitting. See smooth.basisPar

Value

a list with the following entries
1. call- acalltote(ors,t2) using the appropriately constructed covariate and weight matrices
2. argvals- theargvalsargument supplied tolf
3. L- the matrix of weights used for the integration
4. 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 Defines a term $\int_{T}\beta(t)X_i(t)dt$ for inclusion in an gam-formula (or bam or gamm or 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. [object Object],[object Object] fgam, af, mgcv's linear.functional.terms, fgam for examples