similar to: Akaike weight in R

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

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

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

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias

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

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

R Users, I was just wondering if anyone has written a program (or if there is a package) out there that calculates the different derivations of AIC (e.g. AIC, AICc, QAIC, etc.) along with AIC differences (delta's), model likelihoods, Akaike weights and evidence ratio's (from Burnham and Anderson 2002). Just in a "for instance" if someone had the -2LL, sample sizes,

Dear all, I wanted to let you know about four phylogenetic comparative methods (PCM) packages that have become available on (3 on CRAN and 1 on GitHub) recently that hopefully will be interesting to somebody. Three of them go significantly beyond the Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes. 1) There is a new version of mvSLOUCH available. The most important change is that the

Dear all, I wanted to let you know about four phylogenetic comparative methods (PCM) packages that have become available on (3 on CRAN and 1 on GitHub) recently that hopefully will be interesting to somebody. Three of them go significantly beyond the Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes. 1) There is a new version of mvSLOUCH available. The most important change is that the

Dear listers, Here we have a strange result we can hardly cope with. We want to compare a null mixed model with a mixed model with one independent variable. > lmmedt1<-lme(mediane~1, random=~1|site, na.action=na.omit, data=bdd2) > lmmedt9<-lme(mediane~log(0.0001+transat), random=~1|site, na.action=na.omit, data=bdd2) Using the Akaike Criterion and selMod of the package pgirmess

Hello, I am modelling in glmmADMB count data (I´m using a negative binomial distribution to avoid possitive overdispersion) with four fixed and one random effect. I´m also using MuMIn package to calculate the AICc and also to model averaging using the function dredge. What I do not understand is why dredge calculates a different value of the AICc and degrees of freedom than the function AICc

Hello, I'd like to sum the weights of each independent variable across linear models that have been evaluated using AIC. For example: > library(MuMIn) > data(Cement) > lm1 <- lm(y ~ ., data = Cement) > dd <- dredge(lm1, beta = TRUE, eval = TRUE, rank = "AICc") > get.models(dd, subset = delta <4) There are 5 models with a Delta AIC Score of

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

I am new with R and want do some analysis with a point vector data file. Any help is appreciate. Sunny [[alternative HTML version deleted]]

Anyone who can help me with the following question? How can I add weight to [x,y] coordinates on a graph/scatterplot? Background: Monte Carlo simulation generated 730,000 [x,y] coordinates with a weight attached (from 0-0.5). Both x and y are rounded and fit on a raster with x-axis 0-170 months (smalles unit = 1 month) and y-axis 0-6 (smallest unit=0.1). I would like every [x,y] to add its

oops, Forgot to cc to the list. Regards, Mike -----Original Message----- From: dr mike [mailto:dr.mike at ntlworld.com] Sent: 24 February 2005 19:21 To: 'Spencer Graves' Subject: RE: [R] Bayesian stepwise (was: Forward Stepwise regression based onpartial F test) Spencer, Obviously the problem is one of supersaturation. In view of that, are you aware of the following? A Two-Stage

[cc'ing back to r-help: this is good etiquette so that the responses will be seen by others/ archived for future reference.] On 10-08-25 04:35 PM, Marino Taussig De Bodonia, Agnese wrote: > Yes, I meant "MuMIn" > > the global formula I introduced was: > > rc4.mod<-lm(central$hunting~ central$year + central$gender + central$hunter + central$k.score +

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:

I would like test AICc as a criteria for model selection for a glm using stepAIC() from MASS package. Based on various information available in WEB, stepAIC() use extractAIC() to get the criteria used for model selection. I have created a new extractAIC() function (and extractAIC.glm() and extractAIC.lm() ones) that use a new parameter criteria that can be AIC, BIC or AICc. It works as

Dear all, I am trying to do weighted regression using lm function in R. However, I have a question why the results from 1) lm(formula = Y~aX, weight = w) 2) lm(formula = wY~waX) are different. Aren't they supposed to have the exactly same result? Below are the R code to see difference in regression results MatY <- c(0.15,0.42,0.31,0.22) MatX <-