backfitting {pgam} | R Documentation |
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)
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 |
Backfitting algorithm estimates the approximating regression surface, working around the "curse of dimentionality".
More details soon enough.
Fitted smooth curves and partial residuals.
This function is not intended to be called directly.
Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br
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) Modelo Poisson-Gama Semi-Parametrico: Uma Abordagem de Penalizacao por Rugosidade. MSc Thesis. Rio de Janeiro, PUC-Rio, Departamento de Engenharia Eletrica
Hastie, T. J., Tibshirani, R. J.(1990) Generalized Additive Models. Chapman and Hall, London