backfitting: Backfitting algorithm

In pgam: Poisson-Gamma Additive Models

Backfitting algorithm

Description

Fit the nonparametric part of the model via backfitting algorithm.

Usage

backfitting(y, x, df, smoother = "spline",
w = rep(1, length(y)), eps = 0.001, maxit = 100, info = TRUE)

Arguments

y	dependent variable for fitting. In semiparametric models, this is the partial residuals of parametric fit
x	matrix of covariates
df	equivalent degrees of freedom. If NULL the smoothing parameter is selected by cross-validation
smoother	string with the name of the smoother to be used
w	vector with the diagonal elements of the weight matrix. Default is a vector of 1 with the same length of y
eps	convergence control criterion
maxit	convergence control iterations
info	if FALSE only fitted values are returned. It it is faster during iterations

Details

Backfitting algorithm estimates the approximating regression surface, working around the "curse of dimentionality".

More details soon enough.

Value

Fitted smooth curves and partial residuals.

Note

This function is not intended to be called directly.

Author(s)

Washington Leite Junger [e-mail] and Antonio Ponce de Leon [e-mail]

References

Green, P. J., Silverman, B. W. (1994) Nonparametric Regression and Generalized Linear Models: a roughness penalty approach. Chapman and Hall, London

Junger, W. L. (2004) Semiparametric Poisson-Gamma models: a roughness penalty approach. MSc Dissertation. Rio de Janeiro, PUC-Rio, Department of Electrical Engineering.

Hastie, T. J., Tibshirani, R. J.(1990) Generalized Additive Models. Chapman and Hall, London

See Also

pgam, predict.pgam, bkfsmooth

pgam documentation built on Aug. 20, 2022, 1:06 a.m.