similar to: Error message when calculating BIC

Hi All, I am working on some ordinal logistic regresssions using LRM in the Design package. My response variable has three categories (1,2,3) and after using the creating my model and using a call to predict some values and I wanted to use a simple .5 cut-off to classify my probabilities into the categories. I had two questions: a) first, I am having trouble directly accessing the

Good Day All, I have a negative binomial model which I have developed using the MASS library. I now would like to develop some predictions from it. Running the predict.glm (stats library) using type="response" gives me a non-integer value which was rather puzzling. I would like to confirm that this is actually the mean predicted value of the probability mass function as opposed

Good Day, I have several ASCII data files that I would like to import into R. They all have a SAS import file which is used to bring the data into SAS and I am hoping to use this to bring the data into R. There are lots of variables involved and the ASCII data file is 2308 columns long so I would certainly prefer to figure out a smart way of converting the data to R. The ASCII data is a

Good Day All, I am working on some xyplots using the Lattice Library. My X-axis is the date and I am reproducing charts similar to those found in the R Gallery (see here: http://www.sr.bham.ac.uk/~ajrs/R/gallery/plot_midday_weather_profiles.txt) However, the key difference is that some of my data is missing (not collected at that time). For instance, I might have a whole month that I do

Good Day All, I have a negative binomial model that I created using the function glm.nb() with the MASS library and I am performing a cross-validation using the function cv.glm() from the boot library. I am really interested in determining the performance of this model so I can have confidence (or not) when it might be applied elsewhere If I understand the cv.glm() procedure correctly, the

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

Hi, I'm actually trying to rank a set of candidate models with an information criterion (AICc, QIC, BIC). The problem I have is that I use mixed-effect cox regression only available with the package {coxme} (see the example below). #Model1 >spring.cox <- coxme (Surv(start, stop, Real_rand) ~ strata(Paired)+R4+R3+R2+(R3|Individual), spring) I've already found some explications in

Dear R-helpers, I'm considering two methods of selecting a poisson regression model within R: 1. Using the step() function (stats package) to find the best model by a stepwise algorithm and AIC 2. Using the bic.glm() function (BMA package) to find the best model by Bayesian Model Averaging and BIC Are these both reasonable methods for model selection or is one clearly more appropriate than

Hello, I am working on getting optimal lags by using BIC, But I don't know how to calculate BIC. Is there any code or useful function for it? Thanks and regards, Dan Zhao

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]]

Is AIC or BIC available when using the nnet package? Thank you Paul Green

I have been contacted by a researcher who would like to use the bic.logit function (http://lib.stat.cmu.edu/S/bic.logit) for S-PLUS which applies Bayesian Model Averaging to variable selection for logistic regression. I can see that the S-PLUS function uses a call to a Fortran "leaps" function, which does not seem to be available in R. Has this method or a similar method been ported to

Hi All, How can extract AIC,BIC from a fitted Garch model? -- SUMANTA BASAK. [[alternative HTML version deleted]]

Hi, Given a function like x < 0 ? 0 : x We can lower it to bic x, x, asr 31 because we can test if CC==LT && RHS==TrueVal==0 && LHS==FalseVal Further, give a function x > 255 ? 255 : (x < 0 ? 0 :x), we should lower it to: usat x, #8 However, things become more complicated if we have ((x < 0 ? 0 :x) << n ) & mask ... Because it will first be converted to x

I'm using R 2.0.1. I looked in the email archives but didn't see anything on this topic. I've noticed a surprising (to me) difference between AIC and BIC: > methods("AIC") [1] AIC.default* AIC.logLik* > methods("BIC") [1] BIC.gls* BIC.lm* BIC.lme* BIC.lmList* BIC.logLik* BIC.nls* The BIC.gls BIC.lm BIC.lme BIC.lmList and BIC.nls functions appear

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())));

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

Hello I'm with a doubt using BIC and AIC. I want to know if both of then are a way to steem the best model to use. How i know which of then to choose? Talita Perciano Costa Leite Graduanda em Ci??ncia da Computa????o Universidade Federal de Alagoas - UFAL Departamento de Tecnologia da Informa????o - TCI Constru????o de Conhecimento por Agrupamento de Dados - CoCADa

Dear R-helpers I have generated a suite of GLMs. To select the best model for each set, I am using the meta-analysis approach of de Luna and Skouras (Scand J Statist 30:113-128). Simply put, I am calculating AIC, AICc, BIC, etc., and then using whichever criterion minimizes APE (Accumulated Prediction Error from cross-validations on all model sets) to select models. My problem arises where I

I haven't find any helpful thread. How can i calculate BIC and Hosmer-Lemeshow statistic for a logistic regression model. I have used glm for logistic fit. -- View this message in context: http://www.nabble.com/BIC-and-Hosmer-Lemeshow-statistic-for-logistic-regression-tf3945943.html#a11193273 Sent from the R help mailing list archive at Nabble.com.