backf.cl: Classic Backfitting

In RBF: Robust Backfitting

Classic Backfitting

Description

This function computes the standard backfitting algorithm for additive models.

Usage

backf.cl(
  formula,
  data,
  subset,
  point = NULL,
  windows,
  epsilon = 1e-06,
  degree = 0,
  prob = NULL,
  max.it = 100
)

Arguments

formula	an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function was called.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
point	matrix of points where predictions will be computed and returned.
windows	vector of bandwidths for the local polynomial smoother, one per explanatory variable.
epsilon	convergence criterion. Maximum allowed relative difference between consecutive estimates
degree	degree of the local polynomial smoother. Defaults to 0 (local constant).
prob	vector of probabilities of observing each response (length n). Defaults to NULL and in that case it is ignored.
max.it	Maximum number of iterations for the algorithm.

Details

This function computes the standard backfitting algorithm for additive models, using a squared loss function and local polynomial smoothers.

Value

A list with the following components:

alpha	Estimate for the intercept.
g.matrix	Matrix of estimated additive components (n by p).
prediction	Matrix of estimated additive components for the points listed in the argument point.

Author(s)

Matias Salibian-Barrera, [e-mail], Alejandra Martinez

References

Hasie, TJ and Tibshirani, RJ. Generalized Additive Models, 1990. Chapman and Hall, London.

Examples

data(airquality)
tmp <- backf.cl(Ozone ~ Solar.R + Wind + Temp, data=airquality, 
subset=complete.cases(airquality), windows=c(130, 9, 10), degree=1)

RBF documentation built on Sept. 1, 2023, 1:07 a.m.