pgam (version 0.4.1)

bkfsmooth: 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 $y$

Value

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

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.

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