bksmooth: Smoothing of nonparametric terms

In pgam: Poisson-Gamma Additive Models

Smoothing of nonparametric terms

Description

Interface for smoothing functions

Usage

bkfsmooth(y, x, df, smoother = "spline", w = rep(1, length(y)))

Arguments

y	dependent variable for fitting. In semiparametric models, this is the partial residuals of parametric fit
x	independent variable. Univariate fit only
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

Details

Although several smoothers can be used in semiparametric regression models, only natural cubic splines is intended to be used in Poisson-Gamma Additive Models due to its interesting mathematical properties.

Nowadays, this function interfaces the smooth.spline in stats library. It will become not dependent soon enough.

Value

fitted	smoothed values
lev	diagonal of the influence matrix
df	degrees of freedom

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

Hastie, T. J., Tibshirani, R. J.(1990) Generalized Additive Models. 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.

See Also

pgam, predict.pgam

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