R: Generalized Additive Models fitting using penalized regression splines and GCV

gam.fit {mgcv} | R Documentation

## 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 usage see code for `gam`. Some regularization of the IRLS weights is also permitted as a way of addressing identifiability related problems (see `gam.control`). Negative binomial parameter estimation is supported.

### Author(s)

Simon N. Wood simon@stats.gla.ac.uk

### 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, S.N. (2000) Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties. J.R.Statist.Soc.B 62(2):413-428

Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114

Wood, S.N. (in press) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Amer. Statist. Ass.

http://www.stats.gla.ac.uk/~simon/

### See Also

`gam`, `mgcv`, `magic`

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