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 � 12:41, Simon Wood a �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 > > _____________________________________________________________________ > > Simon Wood [EMAIL PROTECTED] 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 > > > ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
