similar to: survival bug?

FWIW Microsoft provides evaluation version of Windows that can be easily installed using VirtualBox and runs for 180 days. One that I believe is the closest to the CRAN setup (Windows 2008) is: https://www.microsoft.com/en-nz/download/details.aspx?id=11093 You just tell VB to setup a Windows 2008 VM then, select the downloaded ISO as CD-ROM drive and install from it. The installation is fairly

I cloned therneau/survival and the installation failed since there is no definition for exported function survfit(). A file seems to be missing - there is survfit0() and survfit0.R but, compared to CRAN, no survfit.R. Georgi Boshnakov ---------------------------------------------------------------------- Message: 1 Date: Thu, 05 Sep 2019 12:53:11 -0500 From: "Therneau, Terry M.,

I treat CRAN as the main repository for survival, but I have also had a github (therneau/survival) version for a couple of years.? It has a vignette2 directory, for instance, that contains extra vignettes that either take too long to run or depend on other packages.? It also gets updated more often than CRAN (though those updates mght not be as well tested yet). In any case, since it is

Hi Terry, http://win-builder.r-project.org/ and the rhub build service (which can be invoked by the rhub package) allow on demand checks in windows environments, though for active debugging the iteration time can be quite painful. If you have access, e.g., through your employer, to a windows license you should also be able to do use VMWare or VirtualBox (I can never remember which one I like

Yes, that is exactly the problem.? The code found in the "config" script is never run.? But why doesn't it get run? On 9/6/19 5:44 AM, Georgi Boshnakov wrote: > I cloned therneau/survival and the installation failed since there is no definition for exported function survfit(). > A file seems to be missing - there is survfit0() and survfit0.R but, compared to CRAN, no

I've never seen this, and have no idea how to reproduce it. For resloution you are going to have to give me a working example of the failure. Also, per the posting guide, what is your sessionInfo()? Terry Therneau On 08/09/2012 04:11 AM, r-help-request at r-project.org wrote: > I have a couple of questions with regards to fitting a coxph model to a data > set in R: > > I have a

Hi all, I found that the two different versions of "survival" packages, namely 2.36-5 vs. 2.36-8 or later, give different results for coxph function. Please see below and the data is attached. The second one was done on Linux, but Windows gave the same results. Could you please let me know which one I should trust? Thanks, ...Tao #####============================ R2.13.0,

It is likely a problem with survival, since 2.9 merged in a large number of changes that had occured in my source tree that had not propogated to the R tree my test suite doesn't have a test for this particular case (2 factors) and - Murphy's law applies: although almost every possible case is covered in the test suite, any new error will hit an omitted combination of

In your particular case I don't think that censoring is an issue, at least not for the reason that you discuss. The basic censoring assumption in the Cox model is that subjects who are censored have the same future risk as those who were a. not censored and b. have the same covariates. The real problem with informative censoring are the covaraites that are not in the model; ones that

Dear all, I am looking for a good reference on "Survival analysis". I am looking for a booking containing both applications and Maths. Explaining different methods in survival analysis .... Many thanks Bernard --------------------------------- [[alternative HTML version deleted]]

> Dear All, > > I have built a survival cox-model, which includes a covariate * time interaction. (non-proportionality detected) > I am now wondering how could I most easily get survival predictions from my model. > > My model was specified: > coxph(formula = Surv(event_time_mod, event_indicator_mod) ~ Sex + > ageC + HHcat_alt + Main_Branch + Acute_seizure +

The combination of survfit, coxph, and factors is getting confused. It is not smart enough to match a new data frame that contains a numeric for sitenew to a fit that contained that variable as a factor. (Perhaps it should be smart enough to at least die gracefully -- but it's not). The simple solution is to not use factors. site1 <- 1*(coxsnps$sitenew==1) site2 <-

> On Jun 1, 2019, at 12:59 PM, Peter Langfelder <peter.langfelder at gmail.com> wrote: > > On Sat, Jun 1, 2019 at 3:22 AM Therneau, Terry M., Ph.D. via R-devel > <r-devel at r-project.org> wrote: >> >> In the next version of the survival package I intend to make a non-upwardly compatable >> change to the survfit object. With over 600 dependent packages

First a statistical issue: The survfit routine will produce predicted survival curves for any requested combination of the covariates in the original model. This is not the same thing as an "adjusted" survival curve. Confusion on this is prevalent, however. True adjustment requires a population average over the confounding factors and is closely related to the standardized

Dear Terry, Even the behaviour of lm() and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance matrix: ---------------- > mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), + data=longley) >

Thanks for the reproducable example. I can confirm that it fails on my machine using survival 2-37.5, the next soon-to-be-released version, The issue is with NextMethod, and my assumption that the called routine inherited everything from the parent, including the environment chain. A simple test this AM showed me that the assumption is false. It might have been true for Splus. Working this

Dear all, I am confused with the output of survfit.coxph. Someone said that the survival given by summary(survfit.coxph) is the baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}. Which one is correct? By the way, if I use "newdata=" in the survfit, does that mean the survival is estimated by the value of covariates in the new data frame? Thank you very much!

Dear R team, dear Prof. Therneau, library(survival) data(colon) ?colon gives me only a very rudimentary source (only a name). Is there a possibility to get a reference to the clinical trial these data are taken from? Many thanks in advance. With best wishes, Matthias Gondan --

I'm working on the next iteration of coxme. (Rather slowly during the summer). This is the most subtle code I've done in S, both mathematically and technically, and seems a perfect vehicle for the "literate programming" paradym of Knuth. The Sweave project is pointed at S output however, not source code. I would appreciate any pointers to an noweb type client that was

Hi, if I got it right then the survival-time we expect for a subject is the integral over the specific survival-function of the subject from 0 to t_max. If I have a trained cox-model and want to make a prediction of the survival-time for a new subject I could use survfit(coxmodel, newdata=newSubject) to estimate a new survival-function which I have to integrate thereafter. Actually I thought