R: Construct an FLM regression term

lf_old {refund}

R Documentation

Construct an FLM regression term

Description

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.

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

`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 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"` integration is always used if `L` is specified
`L`	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 `te` or `s` for details
`presmooth`	logical; if true, the functional predictor is pre-smoothed prior to fitting. See `smooth.basisPar`

Value

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

Author(s)