mgcv (version 0.3-1)

gam.fit: Generalized Additive Models fitting using penalized regression splines and GCV

Description

This is an internal function of package mgcv. It is a modification of the function glm.fit, designed to be called from gam. The major modification is that rather than solving a weighted least squares problem at each IRLS step, a weighted, penalized least squares problem is solved at each IRLS step with smoothing parameters associated with each penalty chosen by GCV or UBRE, using routine mgcv. For further information on usuage see code for gam.

Arguments

WARNING

The code does not check for rank defficiency of the model matrix - it will likely just fail instead!

References

Gu and Wahba (1991) Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method. SIAM J. Sci. Statist. Comput. 12:383-398

Wood (2000) Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties. JRSSB 62(2):413-428

http://www.ruwpa.st-and.ac.uk/simon.html

See Also

gam mgcv