similar to: Breslow Day Test

Dear all, I have been trying to find a function to calculate the Breslow-Day test for homogeneity of the odds ratio in R. I know the test can be preformed in SAS but i was wondering if anyone could help me to perform this in r. In addition i have the fullrefman file to search for functions in the basic R packages, does anyone have any suggestions of an efficient way of searching for

Dear R Users, I'm looking for a package that allows to test hypothesis about a homogeneity of odds ratio in k 2x2 tables. I know that Breslow-Day is suitable but does anybody could me point out a package? I found diffR, but as far as I see this package is for IRT theory. Best, Robert

Hi all, I want to know how to perform the test Breslow-Day test for homogeneity of odds ratios (OR) stratified for svytable. This test is obtained with the following code: epi.2by2 (dat = daty, method = "case.control" conf.level = 0.95, units = 100, homogeneity = "breslow.day", verbose = TRUE) where "daty" is the object type table svytable consider it, but

Hello everyone, Does anyone know if I can do Breslow Day Test for the homogeneity of odds ratio in R? Thanks! - Jacqueline

Hi all, The survivals functions can be tested by the Log-rank test and others, for example the Gehan-Breslow. The graham breslow work with the alpha values. But I don't know how is the Gehan-Breslow test with R. Somebody know a type function?.. or other suggestions? Any help will be really appreciated Jos? Bustos Marine Biologist Master Apllied Stat Program University of Concepci?n

Dear R-users, I am checking the proportional hazard assumption of a cox model for a given covariate, let say Z1, after adjusting for other relavent covariates in the model. To this end, I fitted cox model stratified on the discrete values of Z1 and try to get beslow estimator for the baseline cumulative hazard function (H(t)) in each stratum. As far as i know, if the proportionality assumption

I have a matrix of ceros, for example: 0 0 0 0 0 0 0 0 0 0 0 0 And I would like that one of these values turn into 1, for example if one condition is got, but not in a concrete position if not randomly. Could you indicate me the code to can get it? Thanks in advance, Judit Barroso

Dear all of you, I would like if someone can help me to know how I can read a matrix looking for positions in the matrix that are not null. I have a matrix with most of the "cells" or "positions" null, I am trying to know the invasion power of a plant (number 1 in the matrix when t==1). I would like to continue the model writing some condition when the value of the matrix in

I would like to build a model in R to simulate the seed dispersal by one plant. The plant produced 5 seeds and the probability of falling inside the eight closest space was 0.8 and in the next space 0.2 and in the rest space 0: 0 0 0 0 0 0 0.2 0.2 0.2 0.2 0.2 0 0.2 0.8 0.8 0.8 0.2 0 0.2 0.8 1 0.8 0.2 0 0.2 0.8 0.8 0.8 0.2 0 0.2 0.2 0.2 0.2 0.2 0 0 0 0 0 0 0

Please, I am receiving lot of e-mails that I do not want. Please could you delete my e-mail. Thank, Judit -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Thomas Adams Sent: Monday, December 12, 2011 3:22 PM To: Bert Gunter Cc: r-help at r-project.org Subject: Re: [R] Boxplot of multiple vectors with different lengths Bert,

Relatively new to R, I'm trying to do a relatively simple task. I have data set that has several variables arranged by SubjID and visit, with multiple observations for that combination. I do linear regression on those multiple observations, then generated a set of interpolated values from the regression at fixed intervals along "x". I now want to average each of those across all the

  Hi there,   I am beginner in R and I have some basic question. Suppose I run a common procedure such as a t test or cox model like below:   out<-coxph( Surv(tstart,tstop, death1) ~ x1+x1:log(tstop+1) , test1,method=c("breslow"))    Which yields the following result:   Call: coxph(formula = Surv(tstart, tstop, death1) ~ x1 + x1:log(tstop +     1), data = test1, method =

Dear all of you, I would like to get some help from you. Here I attach you a model, that I would like to be stochastic so I would need each time the value of x changed, the values of parameters (ss, emrg, gf, spp, sr) did too according to a normal distribution, with mean its value and standard deviation given by the parameter name preceded by SDV (In the case of parameter ss, mean is 0.3 and

Dear all, I am struggling with calculation of Schoenfeld residuals of my Cox Ph models. Based on the formula as attached, I calculated the Schoenfeld residuals for both non tied and tied data, respectively. And then I validated my results with R using the same data sets. However, I found that my results for non-tied data was ok but the results for tied data were different from R's. How

For time scale that are truly discrete Cox proposed the "exact partial likelihood". I call that the "exact" method and SAS calls it the "discrete" method. What we compute is precisely the same, however they use a clever algorithm which is faster. To make things even more confusing, Prentice introduced an "exact marginal likelihood" which is not

We occasionally utilize the coxph function in the survival library to fit multinomial logit models. (The breslow method produces the same likelihood function as the multinomial logit). We then utilize the predict function to create summary results for various combinations of covariates. For example:

Hi, I am computing the Nelson-Aalen (NA) estimate of baseline cumulative hazard in two different ways using the "survival" package. I am expecting that they should be identical. However, they are not. Their difference is a monotonically increasing with time. This difference is probably not large to make any impact in the application, but is annoyingly non-trivial for me to just

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

Hello, I am using Dr. Harrell's design package to make a nomogram. I was able to make a beautiful one without stratifying, however, I will need to stratify to meet PH assumptions. This is where I go wrong, but I'm not sure where. Non-Stratified Nomogram:

A.S.: I am sorry for sending my first mail to <r-help at R- project.org>. --------------------------------------------------------- Hallo, computing a Cox proportional hazards model in SPSS 9.0 and R 1.2.2 produces different results for beta-coefficient. I use the follwing data set (source: example in help(coxph), somewhat modified) Time Status Covariate (x)