L1 (Least Absolute Deviation) Functional Regression — fregre.l1 • fdars

Fits a functional linear model using L1 loss (median regression), which is robust to outliers in the response.

Usage

fregre.l1(fdataobj, y, scalar.covariates = NULL, ncomp = 3)

Arguments

fdataobj: An object of class 'fdata'.
y: Response vector.
scalar.covariates: Optional matrix of scalar covariates.
ncomp: Number of FPC components (default 3).

Value

An object of class 'fregre.robust' with components:

intercept: Intercept
beta.t: Functional coefficient beta(t)
fitted.values: Fitted response values
residuals: Residuals
coefficients: Regression coefficients
ncomp: Number of FPC components used
iterations: Number of IRLS iterations
converged: Whether the algorithm converged
weights: Final IRLS weights
r.squared: R-squared statistic
method: Either "l1" or "huber"

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
fit <- fregre.l1(fd, y, ncomp = 3)
# }