backfitting function

Backfitting algorithm

Fit the nonparametric part of the model via backfitting algorithm.

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.

Returns

Fitted smooth curves and partial residuals.

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

Author(s)

Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br

Note

This function is not intended to be called directly.

See Also

pgam, predict.pgam, bkfsmooth