similar to: combined odds ratio

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

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 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)

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

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

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 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,

Hi all, I am trying to fit a logistic regression for a bivariate response using five independent variables in a stepwise procedure. My outputs look okay but does any one know (or is there any literature on) how the confidence intervals are calculated for the reported odds ratios..? Thanks! [[alternative HTML version deleted]]

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 have done a backward stepwise selection on a full binomial GLM where the response variable is gender. At the end of the selection I have found one model with only one explanatory variable (cohort, factor variable with 10 levels). I want to test the significance of the variable "cohort" that, I believe, is the same as the significance of this selected model: >

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 <-

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

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

HI there, i know this is a basic question, though i need some help because this is somewhat away from my current issue, but nevertheless interesting to me... Lets assume i have some estimated probabilities, say estimated by a logit model. i know i can also state them as an odds ratio. Now i?d like to state these odds ratios as a reference to a specific outcome of my investigated

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected? In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.

Hello, I am using the following model model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair), data=MateChoice, REML=F) 1. After reading around through the R help, I have learned that the above code is the right way to analyze a mixed model with the MatingPair as the fixed effect, DrugPair as the random effect and the interaction between these two as the random effect as well.

Peter & Michael, I just came across the following on another mailing list and realized that my use (and the authors of the article use of the term 'odds ratio' ) is probably incorrect. I believe my interest is in the 'odds' of schizophrenia among the population of homicide, rather than a comparison of odds . "Yours seems a good idea, Kevin, if you are only interested

Hello. I'm looking for a method to simulate n different 2x2 tables having all the same odds ratio. For example. I need 100 tables with odds ratio 1 100 tables with odds ratio 2 100 tables with odds ratio 3 and so on. All tables should have the same marginal frequencies. Thank you -- View this message in context:

Hi list, I am trying to calculate the sensitivity, specificity, and odds ratio(and confidence interval). Say, my data looks like this 42.53, 37.56, 40.51, 32.67, 38.19, 81.74, 41.55, 68.94, 59, 63, 54.13, 48.85, 50.46, 51.78 Is there any packages in R that does this? Espeically the odds ratio confidence interval. Mike [[alternative HTML version deleted]]