similar to: code for model-averaging by Akaike weights

Dear folks: In would like to install Sonic Foundry's CDArchitect with wine on my Suse-9.3 box. I have tried it with wine-20050628 (rpm package compiled for Suse-9.3) and older wine releases but all gave the same result. I start the install with: wine sfsetup.exe The install greeting screen comes up and after cliking OK the EULA I get a message that says: "Unable to install software.

Dear folks: I would like to install Endnote 9 trial under wine. According to Frankscorner.org it should work (see http://www.frankscorner. org/index.php?p=endnote9). But when I execute the command: WINEDLLOVERRIDES="msi,msiexec.exe=n" wine ENDemo.EXE, I get the following result: err:module:import_dll Library msi.dll (which is needed by L"C: \\windows\\system\\msiexec.exe")

Dear R users (r-help@r-project.org), The definition of AIC (Akaike information criterion) for normal error models has just been changed. Please refer to the paper below on this matter. Eq.(22) is the new definition. The essential part is RSS(n+q+1)/(n-q-3); it is close to GCV. The paper is temporarily available at the "Papers In Press" place. Kunio Takezawa(2012): A Revision of

I am using lm simulation in R and try to find the AICc and Akaike weight of the model. I searched out that using package "AICcmodavg" AICc is easily to get. I wonder how can I get the "Akaike weight", any function I can use to generate it? Thanks in advance. Sunny [[alternative HTML version deleted]]

Odette > Wondering how can I generate "Akaike weight" with R? I know the description, > but is there any function to generate by R on the web-site or R library? > I am using GLM or GLMM (family=binomial), so would be appreciated if you > help me. You could have a look at this. http://bm2.genes.nig.ac.jp/RGM2/R_current/library/aod/man/summary.aic.html Which is in the OAD

Please can you help me I have the following problem: I have selected an lm model through the step procedure which visualize for each step the AIC value; then I have calculated for the initial model and the selected one the AIC using the funnction AIC. The results are different.What's happened? Emilia Rocco Dipartimento di Statistica "G. Parenti" Università di Firenze e-mail:

Hi everyone! I get the following message anyhow I try to load the package gplots: Error in lazyLoadDBfetch(key, datafile, compressed, envhook) : ReadItem: unknown type 241 Error in library(pkg, character.only = TRUE) : package/namespace load failed for 'gplots' Could any of you tell me what goes wrong? (I am using R 2.0.1. on Windows) Thanks a lot: ----------

Hello, first of all I have found lots of different versions of the FPE which have given me different results. I was wondering if there was an explicit command in R to compute the FPE of a model. Thank you in advance, Jonny -- View this message in context: http://r.789695.n4.nabble.com/Akaike-s-Final-Prediction-Error-FPE-tp4519011p4519011.html Sent from the R help mailing list archive at

Hello all, i hope you don't mind my off topic question. i want to use the Akaike criterion for variable selection in a regression model. Does anyone know some basic literature about that topic? Especially I'm interested in answers to the following questions: 1. Has (and if so how has) the criterion to be modified, if i estimate the transformations of the variables too? 2. How is the

Hello, I am comparing models made with nlme functions and non-nlme functions, based on Akaike's AIC. The AIC values I get for exactly the same model formulation --for example a linear model with no random effects fit with gls and lm, respectively-- do not fit, although the values of the four model parameters are exactly the same. For example: m1 <- gls(height ~ age, data = Loblolly) m2

> -----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 >

Hello I?m need fitt growth curve with data length-age. I want to evaluate which is the function that best predicts my data, to do so I compare the Akaikes of different models. I'm now need to evaluate if changing the initial values changes the parameters and which do not allow to estimate the model. To do this I use the function nls(); and I randomize the initial values (real positive number).

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

?tryCatch -- Sent from my phone. Please excuse my brevity. On October 20, 2017 7:37:12 AM PDT, Evangelina Viotto <evangelinaviotto at gmail.com> wrote: >Hello I?m need fitt growth curve with data length-age. I want to >evaluate >which is the function that best predicts my data, to do so I compare >the >Akaikes of different models. I'm now need to evaluate if changing the

Hello! I am using package "VAR". I've fitted my model: mymodel<-VAR(mydata,myp,type="const") I can extract the Log Liklihood for THE WHOLE MODEL: logLik(mymodel) How could I calculate (other than manually) the corresponding Akaike Information Criterion (AIC)? I tried AIC - but it does not take mymodel: AIC(mymodel) # numeric(0) Thank you! -- Dimitri Liakhovitski

Dear all, is there a way to extract individual likelihoods from a glm/lrm object? By individual likelihoods, I mean the likelihoods whose product give the overall likelihood of the model. I guess the code in the base package, used to compute the Akaike Information Criterion may help me. However, I couldn't figure it out, probably because I'm rather new to likelihood theory and ML

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

hi folks, I'm trying to build a table that contains information about a series of General Linear Models in order to calculate Akaike weights and other measures to compare all models in the series. i have an issue with indexing models and extracting the information (loglikehood, AIC's, etc.) that I need to compile them into the table. Below is some sample code that illustrates my

I am hoping to get some advise on the following: I am looking for an automatic variable selection procedure to reduce the number of potential predictor variables (~ 50) in a multiple regression model. I would be interested to use the forward stepwise regression using the partial F test. I have looked into possible R-functions but could not find this particular approach. There is a function

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