fdars (version 0.3.3)

fregre.np.multi: Nonparametric Regression with Multiple Functional Predictors

Description

Fits a nonparametric regression model with multiple functional predictors using a weighted combination of distance matrices.

Usage

fregre.np.multi(
  fdataobj.list,
  y,
  weights = NULL,
  h = NULL,
  knn = NULL,
  type.S = c("S.NW", "kNN.gCV", "kNN.lCV"),
  Ker = "norm",
  metric = metric.lp,
  cv.grid = NULL,
  cv.folds = 5,
  ...
)

Value

An object of class 'fregre.np.multi' containing:

fdataobj.list

List of functional predictors

y

Response vector

weights

Weights used (or optimized)

weights.cv

CV results if weights = "cv"

fitted.values

Fitted values

residuals

Residuals

D.list

List of distance matrices

D.combined

Combined distance matrix

Arguments

fdataobj.list

A list of fdata objects (functional predictors). All must have the same number of observations.

y

Response vector (scalar).

weights

Weights for combining distances. Can be:

• NULL: Equal weights (1/p for each predictor)

• Numeric vector: Fixed weights (will be normalized to sum to 1)

• "cv": Cross-validate to find optimal weights

h

Bandwidth for Nadaraya-Watson kernel (optional).

knn

Maximum k for k-NN methods.

type.S

Smoother type: "S.NW", "kNN.gCV", or "kNN.lCV".

Ker

Kernel type (default "norm" for Gaussian).

metric

Distance metric function (default metric.lp).

cv.grid

Grid of weight values for CV (only used if weights = "cv"). Default is seq(0, 1, by = 0.1) for 2 predictors.

cv.folds

Number of folds for weight CV (default 5).

...

Additional arguments passed to metric function.

Examples

# Create two functional predictors
set.seed(42)
n <- 50
m <- 30
t_grid <- seq(0, 1, length.out = m)

X1 <- matrix(0, n, m)
X2 <- matrix(0, n, m)
for (i in 1:n) {
  X1[i, ] <- sin(2 * pi * t_grid) * i/n + rnorm(m, sd = 0.1)
  X2[i, ] <- cos(2 * pi * t_grid) * i/n + rnorm(m, sd = 0.1)
}
y <- rowMeans(X1) + 0.5 * rowMeans(X2) + rnorm(n, sd = 0.1)

fd1 <- fdata(X1, argvals = t_grid)
fd2 <- fdata(X2, argvals = t_grid)

# Fit with equal weights
fit1 <- fregre.np.multi(list(fd1, fd2), y)

# Fit with cross-validated weights
fit2 <- fregre.np.multi(list(fd1, fd2), y, weights = "cv")