search for: akaik

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...

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/...

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...

Hello All; I need to run a multiple regression analysis and use Akaike's Information Criterion for model selection. I understand that this command will give the AIC value for specified models: AIC(object, ..., k = 2) with "..." meaning any other optional models for which I would like AIC values. But, how can I specify (in the place of "...&q...

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 Ander...

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]]

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 Nabble.com.

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). To that I put it inside a function that every time...

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...

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:

...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 >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). To that I put it inside a func...

...cope model is derived. A list of user specified lm, glm, lme or nlme objects can alternately be passed. } \value{ A list with the following items: \item{AIC}{a data.frame including LL, the maximized log-likelihood; K the number of estimated parameters; N2K, number of observations/K; AIC, the Akaike index criterion; deltAIC, the difference between AIC and the lowest AIC value; w_i, the Akaike weights; AICc, the second order Akaike criterion; deltAICc, the difference between AICc and the lowest AICc value; w_ic, the AICc weights } \item{models}{the list of models} } \references{ Ander...

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

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 [[alternative HTML version deleted]]

Hello, I'm trying to use AIC to choose between 2 models with positive, continuous response variables and different error distributions (specifically a Gamma GLM with log link and a normal linear model for log(y)). I understand that in some cases it may not be possible (or necessary) to discriminate between these two distributions. However, for the normal linear model I noticed a discrepancy

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 estimation ;-) Thanks for any help/suggestion/tip, Bruno

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 approach so far and my problem. I realiz...

...umber 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 (stepAIC) that uses the Akaike criterion or Mallow's Cp criterion. In addition, the drop1 and add1 functions came closest to what I want but with them I cannot perform the required procedure. Do you have any ideas? Kind regards, Robin Smit -------------------------------------------- Business Unit TNO Automotive Enviro...

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