similar to: Evaluating AIC

Dear all, Using the quasibinomial and quasipoisson families results in no AIC being calculated. However, a quasi AIC has actually been defined by Lebreton et al (1992). In the (in my opinon, at least) very interesting book by Burnham and Anderson (1998,2002) this QAIC (and also QAICc) is covered. Maybe this is something that could be implemented in R. Take a look at page 23 in this pdf:

Hello R-list. I am a "long time listener - first time caller" who has been using R in research and graduate teaching for over 5 years. I hope that my question is simple but not too foolish. I've looked through the FAQ and searched the R site mail list with some close hits but no direct answers, so... I would like to estimate QAIC (and QAICc) for a glm fit using the

Greetings, I have recently come into some confusion over weather or not AIC results for comparing among models requires that they be nested. Reading Burnham & Anderson (2002) they are explicit that nested models are not required, but other respected statisticians have suggested that nesting is a pre-requisite for comparison. Could anyone who feels strongly regarding either position

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

Do any R functions incorporate a sample sample size correction (e.g., Burnham and Anderson 1998). Thanks, Hank Stevens Martin Henry H. Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/botany/bot/henry.html http://www.muohio.edu/ecology

Sorry about re-posting this, it never went out to the mailing list when I posted this to r-help forum on Nabble and was pending for a few days, now that I am subscribe to the mailing list I hope that this goes out: I've been a viewer of this forum for a while and it has helped out a lot, but this is my first time posting something. I am running glm models for richness and abundances. For

Hi All, Sorry for this basic question as I am new to this R. I would like to know, is it possible to consider a matrix with some columns having numeric data and some other's with characters (strings) data? How do I get this type of data from a flat file. Thanks very much, mallika ____________________________________________________________________________ Mallika Veeramalai, Ph.D.,

Hello, I'd like to say that it's clear when an independent variable can be ruled out generally speaking; on the other hand in R's AIC with bbmle, if one finds a better AIC value for a model without the given independent variable, versus the same model with, can we say that the independent variable is not likely to be significant(in the ordinary sense!)? That is, having made a lot of

Dear All, I have a list of models(1000) which have variable scores from 20 different method. I would like to rank models using consensus approach based on high scores from different methods.Is there any function available in R for this purpose? I will appreciate any pointers in this regard. Thank you very much in Advance, Mallika

Dear Listers, I got this message when compiling a package: * creating pgirmess-manual.tex ... OK * checking pgirmess-manual.text ... ERROR LaTex errors when creating DVI version. This typically indicates Rd problems. The message is quite explicit but I struggled a lot before understanding that the trouble comes from a single file "selMod.rd" among 44 topics. Even though I have

In accordance with Venables and Ripley, SAS documentation and other sources AIC with sigma^2 unknown is calculated as: AIC = -2LL + 2* #parameters = n log(RSS/n) + 2p For the fitness data: (http://support.sas.com/ctx/samples/index.jsp?sid=927), SAS gets an AIC of 64.534 with model oxygen = runtime. (SAS STAT User's Guide. Chapter 61. pp 3956, the REG Procedure). This value of AIC accords

Dear all, does anyone have r code to perform model-averaging of regression parameters by Akaike weights, and/or to do all-possible-subsets lm modelling that reports parameter estimates, AICc and number of parameters for each model? I have been looking for these in the archive but found none. (I am aware that many of you would warn me against these methods advocated by Burnham and Anderson

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of B??kony Veronika > Sent: 18 June 2005 14:00 > To: r-help at stat.math.ethz.ch > Subject: [R] how 'stepAIC' selects? > > > Dear all, > Could anyone please tell me how 'step' or 'stepAIC' works? Does it >

Dear R users, x-values (EI) = Adw, EG1, LA1, Ad1, LA2, LA3...(14 levels, insect stages) y-valus = antpop within the boxplot function x-values are ordered alphabetically Idea: x-values ranked by list order (insect stage: Egg stage 1 is followed by Larvae 1 and not by Egg stage 2 as it would be in an alphabetically order) Problems with the order(tapply()) function: variable lengths

Hello, I am comparing 2 nonlinear models one with a correlation structure. I used the gnls command in the nlme package to fit model 1 and then the update(mod1,corr=corExp(form=~lat+lon,nugget=T) to fit the same model but with a correlation structure. I am comparing models using AIC, but I am getting results that don't seem to match. I get matching results for the model with no correlation

Quasi-Poisson regression - using parameter estimates for QAICc Hello, I am using lmer (package lme4), for a GLMM, where I am modeling overdispered data with 1 random effect and several fixed effects. I want to use QAICc for my model selection, however I have 2 concerns 1) I don't know how to properly estimate the overdispersion parameter (c_hat), which is needed to calculate QAICc. I

Hello I've been investigating using multi-model inference, based on calculating AIC and AIC weights, using the techniques outlined in Burnham and Anderson's (2002) book. However I notice a couple of emails in the R-help archive stating that there are errors in the technique. Are these errors associated with the particular implementation that B & A propose in their text, or is the

I have a data file with indicates pretest scores for a linguistics experiment. The data are in long form so for each of 33 subjects there are 400 rows, one for each item on the test, and there is a column called ‘Correct’ that shows ‘C’ for a correct response and ‘E’ for an incorrect response. I am trying to write a formula that will create a vector that indicates the number of correct answers

Hi All, I am wondering if their is a convenient way to export the results of the TukeyHSD function to Word or Excel. I have used capture.output(tukey.contrast, file="tukey.contrast.xls") and this works, but the data are not in a table form, and so it is sort of a pain to manipulate the output. I have be unable to find a more convenient way and any assistance would be greatly

Hi, I would like to obtain the dispersion parameter for a quasipoisson model for later use in calculating QAIC values for model comparison.Can anyone suggest a method of how to go about doing this? The idea I have now is that I could use the residual deviance divided by the residual degrees of freedom to obtain the dispersion parameter. The residual deviance is available in the summary