Functional Linear Model (FPC-based) — fregre.lm • fdars

Fits a functional linear model using FPC regression with the Rust backend. Supports optional scalar covariates.

Usage

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

Arguments

fdataobj: An object of class 'fdata' (functional covariate).
y: Response vector (scalar).
scalar.covariates: Optional matrix of scalar covariates (n x p).
ncomp: Number of FPC components to use. If NULL, uses min(n-1, 15).

Value

A fitted regression object of class 'fregre.lm' with components:

intercept: Intercept term
beta.t: Functional coefficient beta(t) as fdata
gamma: Scalar covariate coefficients
fitted.values: Fitted values
residuals: Residuals
r.squared: R-squared
r.squared.adj: Adjusted R-squared
ncomp: Number of FPC components used
gcv: GCV criterion value

See also

fregre.pc for the R-native FPC regression, fregre.lm.cv for cross-validated component selection

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), nrow = 50), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
result <- fregre.lm(fd, y, ncomp = 3)
result$r.squared
#> [1] 0.02390067
# }