similar to: AIC to compare glm models with Poisson errors?

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

--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

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

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

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 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 *

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

If I have a list I can set a sub-element as follows on the command line: people=list() people$tom$hair="brown" people But what if I have a string containing the name of the sub-element that I want to access? subel= "people$tom$hair" get(subel) # returns error assign(subel,"red") # silent but doesn't change list people The attempts above using assign/get

Hi all, I have a very high dimensional data and apparently there are several columns that contain similar information (some columns are equal). I want to form a matrix/data frame consisting of unique columns. Does anyone have an efficient way of getting out these columns. A small section of the data frame is given below. Thanks for helping. Stephen. > newdata [,1] [,2] [,3] [,4] [,5]

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 all, I have a very basic question: how does the logLik function work for poisson models? Example: I simulate 20 observations from a Poisson distribution with mean 800. y <- rpois(20,800) model <- glm(y ~ 1, family=poisson()) logLik(model) I would like to know what's the exact formula the function logLik uses. I looked at ?extractAIC but I cannot sort it out. Can you please

Dear R Users, I want to test the dimensions of an incoming vector, matrix or array safely and succinctly. Specifically I want to check if the unknown object has exactly 2 dimensions with a specified number of rows and columns. I thought that the following would work: > obj=matrix(1,nrow=3,ncol=5) > identical( dim( obj) , c(3,5) ) [1] FALSE But it doesn't because c(3,5) is numeric

Dear List, I have been trying to use library sm to do density estimation on a 3D dataset. I am using the current MacOS X binary of sm from CRAN. If I do this on a 2D dataset, sm.density returns a list including the component "estimate" which contains the density estimate over a uniform grid. When doing this with 3D data, although I get a nice plot (even when I don't ask for one),

Dear R Users, I have some data that is very similar in form to a 3D image - ie univariate data on a regular 3D grid. I keep this as a 3D numeric array in R with attributes describing the sampling points along the 3 dimensions. I would like to interpolate this onto a new regular 3D grid that I specify (eg by supplying 3 vectors corresponding to the new grid locations on each of the 3

Dear R People, I would like to smooth some 3d lines, which consist of line segments specified by start and end 3d coordinates. These are experimentally derived from microscope images of neurons, so there is no sense in which one axis is any different from any other (predictor vs response). I have tried doing repeated smoothing interpolation with smooth.splines (see below), but this doesn't

Dear R people, I have recently started using rJava to interact with Java code that I have written and am finding it a very useful bridge. Thanks! I have now run into a problem I can't figure out. If I edit and recompile my java source code I would like to force rJava to reload the modified java class. However even doing: .jinit(force.init=TRUE) To restart the jvm doesn't seem to do

Dear All, Does anyone know of any GIS packages for R? I know that there are GIS facilities for S-Plus (eg Arc Info), and am looking for freeware equivalents. Best wishes, Sam Brown ----------------------------------------------------------- Sam Brown sam at zoo.cam.ac.uk Department of Zoology tel +44 1223 330933 Downing Street fax +44 1223 334466