similar to: stepwise selection cox model

Hi all, I have found the following function online median.test<-function(y1,y2){ z<-c(y1,y2) g <- rep(1:2, c(length(y1),length(y2))) m<-median(z) fisher.test(z<m,g)$p.value } in http://www.mail-archive.com/r-help@r-project.org/msg95278.html I have the following data > group1 <- c(2, 2, 2, 1, 4, 3, 1, 1) > group2 <- c(3, 1, 3, 1, 4, 1, 1, 1, 7, 1, 1, 1, 1,

Hello I would like to develop the use of R. Trying R and more particulary the cox model, I am surprised by discrepancies between results with stata and R for a cox model With the same data base, I get a hazard ratio (4.82) that is not the same obtained with stata (4.52) You will find attached the file leukemia.dta I used (Stata) Here are the codes for R library(foreign)

I am calculating cox propotional hazards models with the coxph function from the survival package. My data relates to failure of various types of endovascular interventions. I can successfully obtain the LR, Wald, and Score test p-values from the coxph.object, as well as the hazard ratio as follows: formula.obj = Surv(days, status) ~ type coxph.model = coxph(formula.obj, df) fit =

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

Dear all, I am using the ROCR library to compute the AUC and also the Hmisc library to compute the C-index of a predictor and a group variable. The results of AUC and C-index are similar and give a value of about 0.57. The Wilcoxon p-value is <0.001! Why the AUC is showing small value and the p-value is high significant? The AUC is based on Wilcoxon calculation? Many thanks, Lina

Dear all, I am looking for an R function which does stepwise selection cox model in r (delta chisq likelihood ratio test) similar to the stepwise, pe (0.05) lr: stcox in STATA. I am very thankful for any reply. Regards, Linda [[alternative HTML version deleted]]

Dear all, I am wondering why the step() procedure in R has the description 'Select a formula-based model by AIC'. I have been using Stata and SPSS and neither package made any reference to AIC in its stepwise procedure, and I read from an earlier R-Help post that step() is really the 'usual' way for doing stepwise (R Help post from Prof Ripley, Fri, 2 Apr 1999 05:06:03

Hello, I am constructing a nomogram using cph and nomogram commands in Dr. Harrell's Design/RMS package. The HR that I obtain for dichotomous and categorical variables are identical to those that I obtain using STATA stcox. However, the inter-quartile HR I obtain for continuous variables is obviously different, since STATA gives me HR for each unit (year, centimeter, etc) like coxph would

Hello, How can I run a backward stepwise regression with part of the variables fixed, while the others participate in the backward stepwise analysis? Thank you, Einat -- View this message in context: http://r.789695.n4.nabble.com/Stepwise-analysis-with-fixed-variables-tp4650015.html Sent from the R help mailing list archive at Nabble.com.

I am hoping to get some advise on the following: I am looking for an automatic variable selection procedure to reduce the number of potential predictor variables (~ 50) in a multiple regression model. I would be interested to use the forward stepwise regression using the partial F test. I have looked into possible R-functions but could not find this particular approach. There is a function

Dear all, I am interested to evaluate reclassification using net reclassification improvement and Integrated Discrimination Index IDI after survival analysis (Cox proportional hazards using stcox). I search a R package or a R code that specifically addresses the categorical NRI for time-to-event data in the presence of censored observation and, if possible, at different follow-up time points. I

 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]]

I would like to perform a bootstrap validation of a backward stepwise logistic regression analysis, but I am a beginner with R and I am not sure of how to do it. Is there anyone that can send me a sample file in tab format (that I can modify in Excel by pasting my data) and the pertinent R algorithm? Many thanks Giuseppe -- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Dr. Giuseppe Biondi

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]]

First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure

Dear Everybody: I'm doing my usual "how does that work in R" thing with some Stata projects. I find a gross gap between the Stata and R in Cox PH models, and I hope you can give me some pointers about what goes wrong. I'm getting signals from R/Survival that the model just can't be estimated, but Stata spits out numbers just fine. I wonder if I should specify initial

Is there a function in R which performs stepwise estimation in ways similar to SAS/STATA (i.e., allows the analyst to specify the significance levels for removal/addition of terms). I've been asked to evaluate two final models: one resulting from a backwards selection in R (stepAIC) and one resulting from a backwards selection using PROC LOGISTIC in SAS. The final terms are slightly

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, 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:

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