similar to: [J.Lindsey: Re: glm(.) / summary.glm(.); [over]dispersion and returning AIC..]

I have been implementing a proposal of Jim Lindsey for glm(.) to return AIC values, and print.glm(.) and print.summary.glm(.) printing them.... however: >>>>> "Jim" == Jim Lindsey <jlindsey@luc.ac.be> writes: Jim> The problem still remains of getting the correct AIC when the user Jim> wants the scale parameter to be fixed. (The calculation should

Dear R users, I am using 'R' version 2.2.1 and 'S-PLUS' version 6.0; and I loaded the MASS library in 'S-PLUS'. I am running a logistic regression using glm: --------------------------------------------------------------------------- > mydata.glm<-glm(COMU~MeanPycUpT+MeanPycUpS, family=binomial, data=mydata)

Hi all, I am trying to understand how is calculated the AIC returned by glm(). I have a model object m1 which fitting results are: > summary(m1) [...] (Dispersion parameter for gaussian family taken to be 3.735714) Null deviance: 1439.8 on 15 degrees of freedom Residual deviance: 52.3 on 14 degrees of freedom AIC: 70.357 Since there are 2 parameters, I would naively compute: AIC

Dear list, I am confused about two functions in R: AIC(fm) and extractAIC(fm). What is the difference between two and when do I have to use one over the other? I have found the similar question previously and still not clear for me to understand. I also looked at '?AIC' and '?extractAIC' in R, which is also unclear. I pasted faked data set, fitting summary, and AICs. Thank

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. --27464147-1557463723-1147085467=:8118 Content-Type: TEXT/PLAIN; charset=iso-8859-1; format=flowed Content-Transfer-Encoding: 8BIT On Mon, 8 May 2006, x.sole at iconcologia.net wrote: > Full_Name: Xavier Sol? > Version: 2.3.0 > OS: Windows

Hello, I am using a simple linear model and I would like to get an AIC value. I came across both AIC() and extractAIC() and I am not sure which is best to use. I assumed that I should use AIC for a glm and extractAIC() for lm, but if I run my model in glm the AIC value is the same if I use AIC() on an lm object. What might be going on? Did I interpret these functions incorrectly? Thanks,

I have a question about stepAIC and extractAIC and why they can produce different answers. Here's a stepAIC result (slightly edited - I removed the warning about noninteger #successes): stepAIC(glm(formula = (Morbid_70_79/Present_70_79) ~ 1 + Cohort + Cohort2, family = binomial, data = ghs_70_79, subset = ghs_70_full),direction = c("backward")) Start: AIC=3151.41

Hi, I need to fit models and use AIC method to campare the best fitted model manually. When i extract AIC by using extractAIC, it gave me the df and AIC values. Now the problem is, how can I compare the AIC values from two models? is there anyway to extract AIC with no df so that I can compare directly? Thank you! > extractAIC(coxout) [1] 1.000 1723.038 [[alternative HTML version

I would like to figure out the equations for calculating "AIC" in both "step() function" and "AIC () function". They are different. Then I just type "step" in the R console, and found the "AIC" used in "step() function" is "extractAIC". I went to the R help, and found: "The criterion used is AIC = - 2*log L + k *

Edward, you also can use the package aod on CRAN, see the help page of the function negbin. Best Matthieu An example: > library(aod) > data(dja) > negbin(y ~ group + offset(log(trisk)), ~group, dja, fixpar = list(4, 0)) Negative-binomial model ----------------------- negbin(formula = y ~ group + offset(log(trisk)), random = ~group, data = dja, fixpar = list(4, 0))

Hello- I understand that log-likelihoods are bound to differ by constants, but if i estimate AIC for a set of simple nested linear models using the following 4 methods, shouldn't at least two of them produce the same ordering of models? in R: extractAIC AIC in S-PLUS: AIC n*log(deviance(mymodel)/n) + 2*p I find it troubling that these methods all give me different answers as to the best

I am running a binomial glm with response variable the no of mites of two species y->cbind(mitea,miteb) against two continuous variables (temperature and predatory mites) - see below. My model shows overdispersion as the residual deviance is 48.81 on 5 degrees of freedom. If I use quasibinomial to account for overdispersion the dispersion parameter estimate is 2501139, which seems

Hi, I'd like to hear your opinion about the following proposal to make the computation of dispersion in GLMs more flexible. Dispersion is used in summary.glm; the relevant code chunk with the dispersion calculation is listed below (from glm.R): summary.glm <- function(object, dispersion = NULL, correlation = FALSE, symbolic.cor = FALSE, ...) { est.disp <- FALSE df.r <-

Thank you for your reply. I am aware of the good reasons not to use the deviance estimate in binomial, Poisson, and gamma families. However, for the inverse Gaussian, the choice seems to me less clear cut. So I just wanted to compare two different options. I have used the dispersion parameter to compute the standardized deviance residuals: summary(model.gamma)$deviance.resid

Hello, I am currently working with the betareg package, which allows the fitting of a variable dispersion beta regression model (Simas et al. 2010, Computational Statistics & Data Analysis). I was wondering whether there is any package in R that allows me to fit variable dispersion parameters in the standard logistic regression model, that is to make the dispersion parameter contingent upon

Hi All, could someone shed some light on what the difference between the estimated dispersion parameter that is supplied with the GLM function and the one that the 'gamma.dispersion( )' function in the MASS library gives? And is there consensus for which estimated value to use? It seems that the dispersion parameter that comes with the summary command for a GLM with a Gamma dist. is

I'm confused as to the trustworthiness of the dispersion parameters reported by glm. Any help or advice would be greatly appreciated. Context: I'm interested in using a fitted GLM to make some predictions. Along with the predicted values, I'd also like to have estimates of variance for each of those predictions. For a Gamma-family model, I believe this can be done as Var[y] =

Greetings, I'm posting this OT query here because of out very international membership! In the French sentence "Les taux de tirage sont calcul??s de mani??re ?? ce que la dispersion soit inf??rieure ?? 5 % dans chaque strate." it would seem intended that the "dispersion" is to be calculated in a specific way (unstated) -- otherwise, how to ensure that it shall be

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

I cannot seem to get the aic or extractaic call to work with multinomial logistic regression models. Here is what I am doing: library('Design') lrm1<-lrm(r1~p1) #where p1 is multinomial and r1 is binomial library('MASS') aic(lrm1) Error in if (fam %in% c("gaussian", "Gamma", "inverse.gaussian")) p <- p + : argument is of length zero