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)Fitted smooth curves and partial residuals.
dependent variable for fitting. In semiparametric models, this is the partial residuals of parametric fit
matrix of covariates
equivalent degrees of freedom. If NULL the smoothing parameter is selected by cross-validation
string with the name of the smoother to be used
vector with the diagonal elements of the weight matrix. Default is a vector of \(1\) with the same length of \(y\)
convergence control criterion
convergence control iterations
if FALSE only fitted values are returned. It it is faster during iterations
Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br
Backfitting algorithm estimates the approximating regression surface, working around the "curse of dimentionality".
More details soon enough.
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
pgam, predict.pgam, bkfsmooth