backfitting: Backfitting algorithm

Description Usage Arguments Details Value Note Author(s) References See Also

View source: R/pgam.r

Description

Fit the nonparametric part of the model via backfitting algorithm.

Usage

1
2
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 wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br

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 May 2, 2019, 10:42 a.m.

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