similar to: model selection method - step() or bic.glm()

Hi, I'm trying to using pspline in bic.surv{BMA}. ############################# library(BMA) library(survival) data(veteran) test.bic.surv<- bic.surv(Surv(time,status) ~ karno+pspline(age,df=3)+diagtime+prior, data = veteran, factor.type = TRUE) summary(test.bic.surv, conditional=FALSE, digits=2) ############################# The results are:

Hi all, First: I have a small line of code I'm applying to a variable which will be placed in a matrix table for latex output of accuracy measures: acc.aarima <- signif(accuracy(forecast(auto.arima(tix_ts, stepwise=FALSE), h=365)), digits=3). The time series referred to is univariate (daily counts from 12-10-2010 until 5-8-2010 (so not 2 full periods of data)), and I'm working on

Hi, Is there any package for logistic model selection using BIC and Mallow's Cp statistic? If not, then kindly suggest me some ways to deal with these problems. Thanks. -- View this message in context: http://www.nabble.com/Stepwise-logistic-model-selection-using-Cp-and-BIC-criteria-tf4464430.html#a12729613 Sent from the R help mailing list archive at Nabble.com.

Dear friends, In R, the help of "bic.glm" tells the difference between postmean(the posterior mean of each coefficient from model averaging) and condpostmean(the posterior mean of each coefficient conditional on the variable being included in the model), But it's still unclear about the results explanations, and the artile of Rnews in 2005 on BMA still don't give more detail on

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

Dear R-users, I use glm() to do logistic regression and use stepAIC() to do stepwise model selection. The common AIC value comes out is about 100, a good fit is as low as around 70. But for some model, the AIC went to extreme values like 1000. When I check the P-values, All the independent variables (about 30 of them) included in the equation are very significant, which is impossible, because we

Hi. Background - I am working with a dataset involving around 750K observations, where many of the variables (8/11) are unordered factors. The typical model used to model this relationship in the literature has been a simple linear additive model, but this is rejected out of hand by the data. I was asked to model this via kernel methods, but first wanted to play with the parametric

Hi ! I have been using BMA (bayesian model Averaing) package for modeling purposes, but was faced with a problem of incorporating offset term in the Poisson regression of disease rates. It looks like bic.glm does not accept offset keyword like glm ? Any ways to solve the problem using wt option in BMA? Janne Pitk?niemi -- Department of Public Health P.O.Box 41 (Mannerheimintie 172) 00014

On 2019-12-27 04:34, Duncan Murdoch wrote: > On 26/12/2019 11:14 p.m., Spencer Graves wrote: >> Hello, All: >> >> >> ? ????? The default "simulate" method for lm and glm seems to ignore the >> sampling variance of the parameter estimates;? see the trivial lm and >> glm examples below.? Both these examples estimate a mean with formula = >>

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

Hi All: the package "MuMIn" can be used to select the model based on AIC or AICc. The code is as follows: data(Cement) lm1 <- lm(y ~ ., data = Cement) dd <- dredge(lm1,rank="AIC") print(dd) If I want to select the model by BIC, what code do I need to use? And when to select the best model based on AIC, what the differences between the function "dredge" in

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())));

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?

dears R-users, I'm interested in model selection problem, and i have faced some problems that i would like to ask for help. well, this is a very small example with 4 variable (just one var. is the response - z) with 100 individuals i would like to do a stepwise search, for the "best" model, and a use BIC criteria. I know when I have a lot of variables, let's say 120, I know,

In species distribution modeling where one uses a large sample of background points to capture background variation in presence\pseudo-absence or use\available models (0\1 response) it is frequently recommended that one weight the data so the sum of the absence weights is equal to the sum of presence weights so that the model isn?t swamped by an overwhelming and arbitrary number of background

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]]

On 26/12/2019 11:14 p.m., Spencer Graves wrote: > Hello, All: > > > ????? The default "simulate" method for lm and glm seems to ignore the > sampling variance of the parameter estimates;? see the trivial lm and > glm examples below.? Both these examples estimate a mean with formula = > x~1.? In both cases, the variance of the estimated mean is 1. That's how

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

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

Dear All, I am new to R and I would like to do the following: I want to fit a logistic model with 3 predictors and then perform a stepwise regression to select the best possible model using either the AIC/BIC criterion. I have used the stepAIC function which works fine but using this method only likely candidates are evaluated (i.e. not all the models are fitted). We should have 2^3=8 possible