Huber M-Estimation Functional Regression

Fits a functional linear model using Huber's M-estimation, which provides a smooth compromise between least squares and L1.

Usage

fregre.huber(fdataobj, y, scalar.covariates = NULL, ncomp = 3, k = 1.345)

Arguments

fdataobj

An object of class 'fdata'.

y

Response vector.

scalar.covariates

Optional matrix of scalar covariates.

ncomp

Number of FPC components (default 3).

k

Huber tuning parameter (default 1.345 for 95 percent efficiency).

Value

An object of class 'fregre.robust'. See fregre.l1.

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
fit <- fregre.huber(fd, y, ncomp = 3)
# }