similar to: negative value for AIC and BIC

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 My ultimate goal is to best fit time series by comparing AICs and BICs (as in Bayesian) from arima() and nnet(). I looked at the arima.R source code, but I am afraid I do not understand it. What I only miss really is the number of parameters p, where: AIC = n*log(S/n) + 2*p with S the squared residuals and n the number of observations. Can I get p

Hi all, I would like to know how the BIC criterion is calculated for models estimated using gls( ) function. I read in Pinheiro & Bates (2000) p84 that BIC = -2logL + npar*log(N) (for the ML method), or BIC = -2logLR + npar*log(N-p) (for the REML method) but when I use any of these formulae I don't obtain the result given by R. Thanks in advance for any help. Eve CORDA Office national

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

Dear, When fitting the following model knots <- 5 lrm.NDWI <- lrm(m.arson ~ rcs(NDWI,knots) I obtain the following result: Logistic Regression Model lrm(formula = m.arson ~ rcs(NDWI, knots)) Frequencies of Responses 0 1 666 35 Obs Max Deriv Model L.R. d.f. P C Dxy Gamma Tau-a R2 Brier 701 5e-07 34.49

Dear R community, I would like to compare the degree of aggregation (or dispersion) of bacteria isolated from plant material. My data are discrete counts from leaf washes. While I do have xy coordinates for each plant, it is aggregation in the sense of the concentration of bacteria in high density patches that I am interested in. My attempt to analyze this was to fit negative binomial

I'm wondering whether AIC scores extracted from nls() objects using AIC() are based on the correct number of estimated parameters. Using the example under nls() documentation: > data( DNase ) > DNase1 <- DNase[ DNase$Run == 1, ] > ## using a selfStart model > fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 ) Using AIC() function: >

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

Dear All 1) I'm fitting spatial CAR models using R2Winbugs and although everything seems to go reasonably well (or I think so) the next message appears from WINBUGS 1.4 window: gen.inits() Command #Bugs: gen.inits cannot be executed (is greyed out) The question is if this message means that something is wrong and the results are consequently wrong, or Can I assume it as a simple warning

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 "...") that I want R to

hello, could somebody refer me to the reason R uses -2*loglik + 2*(#param)+2 to calculate AIC? thank you -- Stoyan Iliev

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Hi All,  In order to compare two different logistic regressions, I think I need to compare them based on their BIC values, but I am not sure if the smaller BIC would mean a better model or the reverse is true? Thanks a lot,Andra [[alternative HTML version deleted]]

The function 'regsubsets' appears to calculate a BIC value that is different from that returned by the function 'BIC'. The latter is explained in the documentation, but I can't find an expression for the statistic returned by 'regsubsets'. Incidentally, both of these differ from the BIC that is given in Ramsey and Schafer's, The Statistical Sleuth. I assume

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

In trying to streamline various optimization functions, I would like to have a scratch pad of working data that is shared across a number of functions. These can be called from different levels within some wrapper functions for maximum likelihood and other such computations. I'm sure there are other applications that could benefit from this. Below are two approaches. One uses the <<-

Hi R-users, I have a dataset like this: kuvaaja kuva yhteispisteet Hannu isokala 8 Hannu kaapin alta löytynyt 2 Hannu kaapin alta löytynyt 2 8 Hannu limamikko 1 Hannu maukasta marmeladia 8 Hannu skrinnareita 4 Hate madekoukkujen suojelupyhimys 3 Hate matka aikaan joka ei enää palaa 3 Hate munat puoliks padassa 6 Hate pyynikki 2 Hate vailla armeerausta 2

Do any R functions incorporate a sample sample size correction (e.g., Burnham and Anderson 1998). Thanks, Hank Stevens Martin Henry H. Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/botany/bot/henry.html http://www.muohio.edu/ecology

Hi R-users, I have a data frame like this: photographer category picture points Hannu kalat limamikko 1 Teemu kalat verkkovaja 3 Hate kalat munat puoliks padassa 6 Hannu kalat isokala 8 Teemu kasvit, sienet ja muut eliöt harppi 2 Hate kasvit, sienet ja muut eliöt pyynikki 2 Petteri kasvit, sienet ja muut eliöt harmaaleppä 5 Lauri kasvit, sienet ja muut eliöt lumipuu 9 Teemu linnut kainostelua 1

I am analysing binomial data using a generalised mixed effects model. I understand that if I use glmmPQL it is not appropriate to compare AIC values to obtain a minimum adequate model. I am assuming that this means it is also inappropriate to use AIC values from lmer since, when analysing binomial data, lmer also uses PQL methods. However, I wasn't sure so please could somebody clarify