similar to: Inconsistency in AIC values for glm with family poisson (PR#8841)

--Multipart_Wed_Feb__4_12:25:40_1998-1 Content-Type: text/plain; charset=US-ASCII Jim, I am relating your message to R-devel. This should be discussed in a broader audience; I am not an expert on GLM's, I know you are and others on this group also... R-develers, please CC to Jim Lindsey (on this topic), since he hasn't been part of the R-devel list for a while.. BTW: I will be gone

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 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

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,

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)

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 have used the glm function to fit a series of models using a poisson error structure. e.g: Model 1: Y is a function of a + bX Model 2: Y is a function of a I have tried to compare models using AIC, but do not get sensible results (lower AICs for the null, intercept only, model despite the alternate model containing highly significant parameters). I found the following explanation in the

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 *

Apreciados miembros de lista, nos complace anunciaros la realización de la 6ª Edición del CURSO DE INTRODUCCIÓN A R, organizado por la Unidad de Biomarcadores y Susceptibilidad del Institut Català d''Oncologia. El curso tendrá lugar en Barcelona, los días 19, 20, 21 y 22 de Septiembre de 2011. El curso está abierto a toda persona interesada, sin necesidad de conocimientos de

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

Hi! The nobs() method for glm objects always returns the number of cases with non-null weights in the data, which does not correspond to the number of observations for Poisson regression/log-linear models, i.e. when family="poisson" or family="quasipoisson". This sounds dangerous since nobs() is, as the documentation states, primarily aimed at computing the Bayesian

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

Following p.206 of "Statistical Models in S", I wish to change the code for summary.glm() so that it estimates the dispersion for binomial & poisson models when the parameter dispersion is set to zero. The following changes [insertion of ||dispersion==0 at one point; and !is.null(dispersion) at another] will do the trick: "summary.glm" <- function(object, dispersion =

There may be a bug in the anova.glm function. deathstar[32] R R : Copyright 2004, The R Foundation for Statistical Computing Version 2.0.1 (2004-11-15), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project

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

Dear all,   I want to calculate AIC values of PLSR models. But I find that AIC and extractAIC functions in R could not be used to calculate AIC values of PLSR models. Now I write a section of code(below) to calculate it. But I don't known whether the result is right or not. If I am wrong, please give me some suggestions. Thanks a lot.   Rong Huang   data<-data.frame(

Leandra Desousa <sousa <at> ims.uaf.edu> writes: >> > 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: >> > >> > >summary(mydata.glm) >> > Call: >> > glm(formula = COMU ~

Dear R users, According Brockwell & Davis (1991, Section 9.3, p.304), the penalty term for computing the AIC criteria is "p+q+1" in the context of a zero-mean ARMA(p,q) time series model. They arrived at this criterion (with this particular penalty term) estimating the Kullback-Leibler discrepancy index. In practice, the user usually chooses the model whose estimated index is

Hi Community, I'm trying to reconcile Cook's Distances computed in glm. The following snippet of code shows that the Cook's Distances contours on the plot of Residuals v Leverage do not seem to be the same as the values produced by cooks.distance() or in the Cook's Distance against observation number plot. counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9)