Hello Minda, If you are interested on average different models in a Multi-model inference framework (this is what the AICcmodavg package does) I would recommend you a new R package called "glmulti". I have been using multi-model inference quite a lot recently and this package is definitely the most useful I have been able to find. The implementation of the package is also very simple if you follow the provided instructions.
Hope this helps, best wishes, Enric Frago -- Enric Frago University of Oxford Department of Zoology The Tinbergen Building, South Parks Road Oxford, OX1 3PS [email protected] +44(0)1865281079 https://sites.google.com/site/plantmicrobeinsect/ > On Sun, 2011-11-13 at 11:31 -0600, Stephen Sefick wrote: > > Vegan (on CRAN) may be of help. Particularly look at the ordistep, > > ordiR2step etc. > > But do note the warnings that as these models don't really have a log > likelihood and hence the don't have a deviance nor AIC. The AIC > implemented in vegan uses the method of: > > GodÃnez-DomÃnguez, E. & Freire, J. (2003) Information-theoretic > approach for selection of spatial and temporal models of community > organization. _Marine Ecology Progress Series_ *253*, 17-24. > > Read Jari's warnings in ?deviance.cca regarding its usage. > > ordiR2step uses the forward selection method of: > > Blanchet, F. G., Legendre, P. & Borcard, D. (2008) Forward > selection of explanatory variables. _Ecology_ 89, 2623-2632. > > which employs and adjusted R^2 criterion. > > Note that any form of forward selection applied to these multivariate > methods is just as likely to be subject to all the problems of stepwise > selection methods familiar to the application of linear regression. It > would be helpful if we could combine these ordination methods with the > concept of shrinkage (e.g. the lasso) so that selection could be > performed in a single step *and* the effects of selection be taken into > account. (There has been some progress in this regard in some > [non-ecological] parts of the literature.) > > Or, better still, think before fitting the model and only include those > terms that you wish to test that correspond to the hypotheses you wish > to test. > > HTH > > G > > > On Sun 13 Nov 2011 01:25:46 AM CST, David_Hewitt wrote: > > > > > > On Sat, 12 Nov 2011, Michel Rapinski<[email protected]> wrote: > > >> > > >> Hello, > > >> > > >> There is a function in R's basic library (stats), step(), which allows > > >> step by step selection of variables (forward, backward, both) on multiple > > >> linear regression models based on AIC scores. > > >> > > >> Unfortunately, and correct me if I am wrong, it only works for lm, > > >> aov and > > >> glm models. > > > > > > > > > The package AICcmodavg handles many other types of linear models. It's > > > on CRAN. > > > > > >> > > >> In the case of selecting variables for canonical analysis, > > >> more specifically redundancy analysis (RDA), are there functions that > > >> enables these same test on rda models? I figured that since RDA is > > >> basically a multivariate extension of the multiple linear regression, it > > >> should work, but no luck! > > > > > > > > > There are important differences between ordination and linear models. > > > Beyond > > > that, the issue of selecting important variables is far more complex than > > > just an automated routine to search through them for "significance" > > > (of any > > > kind). Patrick's recommendation to have a look at the book by Burnham and > > > Anderson is a good one -- start with pp 84-85 and section 4.4 (pp 167 ->). > > > > > >> > > >> I have succesfully managed to use the forward.sel() function in > > >> library(packfor), for selecting variables in my multivariate RDA models, > > >> but I also wish to do backward and alternating selection to help in the > > >> selection of my variables. > > >> > > >> Help will be greatly appreciated. > > >> > > >> Michel > > >> > > >> Michel Rapinski, MSc. Student > > >> Inst. of Plant Biology Research, Montreal Botanical Garden > > >> Université de Montréal > > >> Montréal, QC H1X 2B2 > > >> Tel: 514.772-1710 > > >> Fax: 514.872.9406 > > >> [email protected] > > >> University of Ottawa > > >> [email protected] > > >> > > >>> > > >>> Hi Minda, > > >>> > > >>> AIC scores depend upon the statistical models used. I think R does the > > >>> best job of providing these scores, for example in the context of > > >>> multiple > > >>> linear regression and generalized linear models. > > >>> > > >>> The literature on R or on stats using R is growing rapidly. You will > > >>> find > > >>> readable treatments of AIC in Crawley's 2007 R book or in Zuur et > > >>> al'sv2009 Mixed Effects Models and extensions in Ecology with R. > > >>> > > >>> And do not forget to examine ( I am not sure read is a realistic option) > > >>> the valuable book by Burnham and Anderson 2002, Models Selection and > > >>> Multimodel Inference. > > >>> > > >>> > > >>> Patrick Foley > > >>> bees, fleas, flowers, disease > > >>> [email protected] > > >>> ________________________________________ > > >>> From: Minda Berbeco [[email protected]] > > >>> Sent: Wednesday, October 26, 2011 8:32 AM > > >>> To: [email protected] > > >>> Subject: [ECOLOG-L] AIC scores > > >>> > > >>> Hello, > > >>> > > >>> I am looking for recommendations for programs to use for calculating AIC > > >>> scores. I've looked into the AICcmodavg package with R, but the > > >>> associated > > >>> instructional material is not clear and I have not been able to get > > >>> it to > > >>> work. I hear that SAS is good as well, but have not found a good book > > >>> that > > >>> tells me how to create AIC scores (recommendations would be > > >>> appreciated). > > >>> I've also looked into SPSS, which according to IBM can create AIC > > >>> scores, > > >>> but have had no success. > > >>> > > >>> Any recommendations for programs and clear associated instructional > > >>> material > > >>> with information on how to run the program, write the code etc. would be > > >>> greatly appreciated. > > >>> > > >>> Thanks, > > >>> > > >>> Minda Berbeco > > >>> Viticulture and Enology, UC Davis > > >>> [email protected] > > > > -- > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% > Dr. Gavin Simpson [t] +44 (0)20 7679 0522 > ECRC, UCL Geography, [f] +44 (0)20 7679 0565 > Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk > Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ > UK. WC1E 6BT. [w] http://www.freshwaters.org.uk > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% > >
