Elastic Regression — elastic.regression • fdars

Alignment-integrated regression and classification for functional data. Elastic Scalar-on-Function Regression

Usage

elastic.regression(
  fdataobj,
  y,
  ncomp.beta = 10,
  lambda = 0,
  max.iter = 20,
  tol = 1e-04
)

Arguments

fdataobj: An object of class 'fdata'.
y: Response vector (numeric).
ncomp.beta: Number of basis functions for the beta coefficient (default 10).
lambda: Regularization parameter (default 0).
max.iter: Maximum iterations for the alignment-regression loop (default 20).
tol: Convergence tolerance (default 1e-4).

Value

An object of class 'elastic.regression' with components:

alpha: Intercept
beta: Beta coefficient function
fitted.values: Fitted response values
residuals: Residuals
sse: Sum of squared errors
r.squared: R-squared
gammas: Estimated warping functions
aligned.srsfs: Aligned SRSF transforms
n.iter: Number of iterations to convergence
fdataobj: Original functional data
y: Response vector

Details

Fits a functional linear model with simultaneous elastic alignment of the functional covariate. The alignment and regression are jointly optimized.

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
fit <- elastic.regression(fd, y)
fit
#> Elastic Scalar-on-Function Regression
#>   Intercept: -0.1284 
#>   R-squared: 0.4059 
#>   Iterations: 20 
# }