[R] Gam() function in R
ym at climpact.com
Mon Dec 6 17:35:59 CET 2004
so mgcv package is the one i need! indeed, i want integrated smoothness
selection and smooth interactions rather than stepwise selection. i have
a lot of predictor, and i use gam to select those who are "efficient"
and exclude others. (using p-value)
thanks a lot for those precious information.
Le lun 06/12/2004 Ã 12:41, Simon Wood a Ã©crit :
> > this subject is very intersting for me. I'm using mgcv 0.8-9 with R
> > version 1.7.1. i didn't know that there was an another gam version with
> > package library(gam). Someone can tell me the basics differences between
> > them? I look for an help page on google but i only find "mgcv" help
> > pages.
> - I think you'd need to move to a newer version of R in order to use
> package gam, but that would also let you use a much more recent version of
> package mgcv.
> - package gam is based very closely on the GAM approach presented in
> Hastie and Tibshirani's "Generalized Additive Models" book. Estimation is
> by back-fitting and model selection is based on step-wise regression
> methods based on approximate distributional results. A particular strength
> of this approach is that local regression smoothers (`lo()' terms) can be
> included in GAM models.
> - gam in package mgcv represents GAMs using penalized regression splines.
> Estimation is by direct penalized likelihood maximization with
> integrated smoothness estimation via GCV or related criteria (there is
> also an alternative `gamm' function based on a mixed model approach).
> Strengths of the this approach are that s() terms can be functions of more
> than one variable and that tensor product smooths are available via te()
> terms - these are useful when different degrees of smoothness are
> appropriate relative to different arguments of a smooth.
> Here's an attempt at a summary of the differences:
> Estimation: gam::gam based on backfitting, mgcv::gam based on direct
> penalized likelihood maximization (with smoothness estimation integrated)
> Model selection: package(gam) based on stepwise regression methods.
> mgcv::gam based on integrated GCV estimation of degree of smoothness.
> Smooth terms: gam::gam can represent smooth terms using a very wide range
> of scatterplot smoothers incuding loess, which is built in. mgcv::gam is
> restricted to smoothers that can be represented using basis functions and
> an associated ``wiggliness'' penalty, but these include low rank thin
> plate spline smoothers and tensor product smoothers for smooths of more
> than one variable. Both packages provide interfaces for adding new classes
> of smoother.
> Uncertainty estimation: since mgcv GAMs explicitly estimate
> coefficients for each smooth term, it is fairly straightforward to obtain
> a covariance matrix for the model coefficients, which makes further
> variance calcualtions easy. For example predictions with standard errors
> are easily obtained for predictions made with new prediction data. The
> backfitting approach makes variance calculation more difficult (e.g. at
> present s.e.s are not available from gam::predict.gam with new data)
> Interface: both packages are based on Trevor Hastie's Chapter 7 of
> Chambers and Hastie. Since Trevor H. wrote package(gam) it's a closer
> implementation than package(mgcv).
> Basically, if you want integrated smoothness selection, an underlying
> parametric representation, or want smooth interactions in your models
> then mgcv is probably worth a try (but I would say that). If you want to
> use local regression smoothers and/or prefer the stepwise selection
> approach then package gam is for you.
> > Simon Wood simon at stats.gla.ac.uk www.stats.gla.ac.uk/~simon/
> >> Department of Statistics, University of Glasgow, Glasgow, G12 8QQ
> >>> Direct telephone: (0)141 330 4530 Fax: (0)141 330 4814
More information about the R-help