similar to: bic.logit

Hi Folks, I've got a question concerning the calculation of the Schwarz-Criterion (BIC) done by summary.regsubsets() of the leaps-package: Using regsubsets() to perform subset-selection I receive an regsubsets object that can be summarized by summary.regsubsets(). After this operation the resulting summary contains a vector of BIC-values representing models of size i=1,...,K. My problem

Dear friends. On the Bayesian averaging homepage http://www.research.att.com/~volinsky/bma.html I found some S code some of which perhaps may run in R. There was a call to an algorithm possibly within S but not supported by R 64.1: "leaps and bounds". I guess it is a minimization step. Can anyone clarify the algorithm and perhaps even give a pointer to some code ? I guess this may be

DeaRs, I have a doubt about: BIC (Bayesian Information Criterion) SBIC (Schwartz Bayesian Informarion Criterion) SIC (Schwartz Information Criterion) In many references these are know as the same (eg. stepAIC() function) but I just found a SAS8.2 output that show either the BIC and SIC values for a logistic regression.. simillary values but different. 1) question: What are the differences?

Dear all, is there a function similar to extractAIC based on which I can extract the BIC (Bayesian Information Criterion) of a coxph model? I found some functions that provide BIC in other packages, but none of them seems to work with coxph. Thanks, Michael [[alternative HTML version deleted]]

Hi All,  After fitting a model with glm function, I would like to do the model selection and select some of the features and I am using the "step function" as follows:  glm.fit <- glm (Y ~ . , data = dat, family = binomial(link=logit)) AIC_fitted = step(glm.fit, direction = "both") I was wondering is there any way to select the features based on BIC rather than AIC? is there

Hello, Is there a function in R by which one can run AR model with Bayesian Information Criteria (BIC)? To my knowledge, functions ar and ar.ols could select the order only by AIC. Thanks, Miao [[alternative HTML version deleted]]

Dear R-List I have a question about how to interpret BIC as a goodness-of-fit statistic. I was trying to use "EMclust" and other "mclust" library and found that BIC was used as a goodness-of-fit statistic. Although I know that smaller BIC indicates a better fit, it is not clear to me how good a fit is by reading a BIC number. Is there a standard way of interpreting a BIC

Full_Name: Ju-Sung Lee Version: 2.2.0 OS: Windows XP Submission from: (NULL) (66.93.61.221) BIC() requires the attribute $nobs from the logLik object but the logLik of a glm(formula,family=binomial()) object does not include $nobs. Adding attr(obj,'nobs') = value, seems to allow BIC() to work. Reproducing the problem: library(nmle); BIC(logLik(glm(1~1,family=binomial())));

Dear all, I am writing an R code to fit a Bayesian mixed logit (BML) via MCMC / MH algorithms following Train (2009, ch. 12). Unfortunately, after many draws the covariance matrix of the correlated random parameters tend to become a matrix with almost perfect correlation, so I think there is a bug in the code I wrote but I do not seem to be able to find it.. dull I know. Has anybody written a

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

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

Dear R Users, I am looking for an alternative to AIC or BIC to choose model parameters. This is somewhat of a general statistics question, but I ask it in this forum as I am looking for a R solution. Suppose I have one dependent variable, y, and two independent variables, x1 an x2. I can perform three regressions: reg1: y~x1 reg2: y~x2 reg3: y~x1+x2 The AIC of reg1 is 2000, reg2 is

Is there an efficient way to do linear model selection by choosing the model with the highest BIC from all possible models? ______________________________________________________________________ Stuart Luppescu -=-=- University of Chicago $(B:MJ8$HCRF`H~$NIc(B -=-=- s-luppescu at uchicago.edu http://www.consortium-chicago.org/people/sl.html http://musuko.uchicago.edu/pubkey.asc

Posted about this earlier. Didn't receive any response But, some further research leads me to believe that MAYBE a GLMM or a GEE function will do what I need. Hello, I have a bit of a tricky puzzle with trying to implement a logit model as described in a paper. The particular paper is on horseracing and they explain a model that is a logit trained "per race", yet somehow the

Hi all, I obtained negative values for AIC and BIC criteria for a particular model that I have developped... I don't remember to have negative values for these crietria for others applications, so I am a little suprised... Could anyone tell me if something is wrong or his conclusion concerning my model? Best regards, Olivier.

Hello, I have a question about doing ALL possible subsets regression with a general linear model. My goal is to produce cumulative Akaike weights for each of 7 predictor variables-to obtain this I need R to: 1. Show me ALL possible subsets, not just the best possible subsets 2. Give me an AIC value for each model (instead of a BIC value). I have tried to do this in library(RcmdrPlugin.HH),

Hi everyone, I am wondering if there exists a stepwise regression function for the Bayesian regression model. I tried googling, but I couldn't find anything. I know "step" function exists for regular stepwise regression, but nothing for Bayes. Thanks -- View this message in context: http://www.nabble.com/Bayesian-regression-stepwise-function--tp26013725p26013725.html Sent from

Hi I am wanting to fit some multinomial logit models (multinom command in package nnet) Is it possible to do any model checking techniques on these models e.g. residual, leverage etc. I cannot seem to find any commands that will allow me to do this. Many thanks ---------------------- L.E.Gross L.E.Gross at maths.hull.ac.uk

-----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

Hello everyone, I am searching for a covariate selection procedure in a cox model formulated as a counting process. I use intervals, my formula looks like coxph(Surv(start,stop,status)~ x1+x2+...+cluster(id),robust=T) where id is a country code (I study occurence of civil wars from 1962 to 1997). I'd like something not based on p-values, since they have several flaws for this purpose. I turned