pgam (version 0.4.1)

backfitting: 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

Value

• Fitted smooth curves and partial residuals.

Details

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

More details soon enough.

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