Elastic Principal Component Regression

Fits scalar-on-function regression using elastic FPCA scores as predictors.

Usage

elastic.pcr(
  fdataobj,
  y,
  ncomp = 3,
  pca.method = c("vertical", "horizontal", "joint"),
  lambda = 0,
  max.iter = 20,
  tol = 1e-04
)

Arguments

fdataobj

An object of class 'fdata'.

y

Response vector (numeric).

ncomp

Number of principal components (default 3).

pca.method

PCA decomposition method: "vertical" (amplitude), "horizontal" (phase), or "joint" (combined).

lambda

Regularization parameter for alignment (default 0).

max.iter

Maximum alignment iterations (default 20).

tol

Convergence tolerance (default 1e-4).

Value

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

alpha

Intercept

coefficients

PC regression coefficients

fitted.values

Fitted response values

sse

Sum of squared errors

r.squared

R-squared

pca.method

PCA method used

karcher.mean

Karcher mean curve

vert.scores

Vertical FPCA scores (if applicable)

horiz.scores

Horizontal FPCA scores (if applicable)

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
fit <- elastic.pcr(fd, y, ncomp = 2)
fit
#> Elastic Principal Component Regression
#>   PCA method: vertical 
#>   Components: 2 
#>   R-squared: 0.08287 
# }