similar to: Poisson regression with GEE using stepwise selection of independent variables.

Hi, How to perform a stepwise selection of the best models for gee? Why can't step() do this job? Thanks. Sincerely Yours, Jinn-Yuh Guh, M.D. Dept. of Internal Medicine Kaohsiung Medical College 100 Shi-Chuan 1st Road Kaohsiung, Taiwan FAX: 886-7-312-2810 e-mail: jyuh at mail.nsysu.edu.tw -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hi , I use library(gee),library(geepack),library(yags) perform GEE data analysis , but all of them cannot do variable selection! Both step and stepAIC can do variable selection based on AIC criterion under linear regression and glm, but they cannot work when model is based on GEE. I want to ask whether any variable selection function or package under GEE model avaliable now? Thanks! Best,

I use the step() function occasionally, and I think I understand its objective, proper use, and limitations. Now I see stepwise model selection being used in what seems to be an unusual way, and I wonder if it is right or wrong. May I describe? Genetic mapping tries to find where in an animal's genome are genetic elements that influence a particular physical trait. Say there are 100

 Hi, Is there any function available to do stepwise selection of variables in Conditional(matched) logistic regression( clogit)? step, stepwise  etc are failing in case of conditional logistic regression. Please help.  Thanks P.T. Subha [[alternative HTML version deleted]]

Sorry, I have wrote a wrong subject in the first email! Regards, Linda ---------- Forwarded message ---------- From: linda Porz <linda.porz@gmail.com> Date: 2011/5/25 Subject: combined odds ratio To: r-help@r-project.org Cc: r-help-request@stat.math.ethz.ch Dear all, I am looking for an R function which does stepwise selection cox model in r (delta chisq likelihood ratio test) similar

windows Vista R 2.10.1 Is it possible to get p values from gee? Summary(geemodel) does not appear to produce p values.: > fit4<- gee(y~time, id=Subject, data=data.frame(data)) Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 running glm to get initial regression estimate (Intercept) time 1.1215614 0.8504413 > summary(fit4) GEE: GENERALIZED LINEAR MODELS FOR

Dear R-Help, My problem/bug came to light,when fitting a linear model using stepwise selection. I'd started with the straightfoward command step(lm(y~., dataset)) This worked fine, but because this starts with all the possible explanatory variables, it results in a model with too many explanatory variables. Hence I wanted to start with just a constant and do forward selection, to get a

oops, Forgot to cc to the list. Regards, Mike -----Original Message----- From: dr mike [mailto:dr.mike at ntlworld.com] Sent: 24 February 2005 19:21 To: 'Spencer Graves' Subject: RE: [R] Bayesian stepwise (was: Forward Stepwise regression based onpartial F test) Spencer, Obviously the problem is one of supersaturation. In view of that, are you aware of the following? A Two-Stage

Hi All, I know in R there is function named 'step', which does the stepwise regression and choose the model by AIC. However, if I want to choose a model per this logic: 1. Run a full model (linear regression, f = lm(y ~., data = ZZZ), for example) 2. Pick up the variable with biggest p value, delete it from the module and get a new regression model. 3. Repeat step 2

Hello, I'm actually using the "Step" procedure in R for multiple regression analysis. I'm using the stepwise selection which alternates between forward selection and backward elimination (direction "both" in the step procedure). I would like to know which F-levels R is using to enter and then to remove variables? I also would like which is the procedure to change

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.

Hi all, I am running Stepwise logistic regression and i have : 1- Multiple covatiates included in each model (No missing data) 2- Genotype data (SNPs) about 500,000 . I partitioned the data to multiple files (there are missing data) I run the step by including all the covariates and one SNP at each model. but i got this message : number of rows in use has changed: remove missing values? In

Dear list, I wanna select the significant variables relative to bird distribution, using stepwise method. However, the result is always the best-fit model. Please kindly suggest if it is possible to show the selection process. Thank you Elaine [[alternative HTML version deleted]]

Good Day, I fit a multivariate linear regression model with 3 dependent variables and several predictors using the lm function. I would like to use stepwise variable selection to produce a set of candidate models. However, when I pass the fitted lm object to step() I get the following error: Error from R: Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, : no

I am using VEGAN ordistep function for stepwise model selection. By default the Pin and Pout values are set to .05 and .1 Is it appropriate to use a family wise correction ( such as bonferroni or one of the alternatives) to adjust these values where there are several (5-10), potentially correlated variables in the model selection process? -- Nevil Amos Molecular Ecology Research Group

hello all, I've been trying for some time to try and model a big dataset using generalized linear models, or more precisely negative binomial regression. Unfortunately i have too many explanatory variables and i'd like to cut some of them off using a stepwise selection method. i've been struggeling with the code for quite a long period and can't find a way to do this....any ideas?

Hi, I looking for a method that use F or Chisq test instead of AIC in a stepwise modelo selection. I try the grasp package using the grasp.step.anova, but It dont work. > library(grasp) Carregando pacotes exigidos: gam Carregando pacotes exigidos: splines Carregando pacotes exigidos: mda Carregando pacotes exigidos: class > data(anorexia,package="MASS") > > m1 <-

Hello, I have a question pertaining to the stepwise regression which I am trying to perform. I have a data set in which I have 14 predictor variables accompanying my response variable. I am not sure what the difference is between the function "mle.stepwise" found in the wle package and the functions "step" or "stepAIC"? When would one use

Dear Gurus, Thank you in advance for your assistance. I'm trying to understand scope better when performing stepwise regression using "step." I have a model with a binary response variable and 10 predictor variables. When I perform stepwise regression I define scope=.^2 to allow interactions between all terms. But I am missing something. When I perform stepwise regression (both

Hi, S-PLUS includes the function "stepwise" which can use a variety of methods to conduct stepwise multiple linear regression on a set of predictors. Does a similar function exist in R? I'm having difficulty finding one. If there is one it must be under a different name because I get an error message when I try 'help(stepwise)' in R. Thanks for your help, Andy Taylor