search for: coxs

library(survival) library(boot) data=NULL lambda=NULL result=NULL pat=rep(1:102,each=1) trt=rep(c(1,0),51) status=rep(1,102) site=rep(1:51, each=2) nr.datasets=100 seed=2006 beta=log(1/2) for (i in 1:51) { lambda[i]=1+((3-1)/50)*(i-1)} lambda1=rep(lambda, each=2) dummy=rep(c(exp(beta),1),51) elf=lambda1*dummy r=70 #the number of bootstrap replicates

Dear R-people: I tried to create a shared library in Windows XP. However I got error messages which attached below: C:\lasso>Rcmd SHLIB all.f cox.f gcc all.o libR makeMakedeps all.dll -o all gcc.exe: libR: No such file or directory gcc.exe: makeMakedeps: No such file or directory make: *** [all] Error 1 I have created shard libraries successfully before. Also for the same fortran files:

Hi I am writing an R library. The documentation for one of my functions includes an example that I *know* works - simply cut and paste into R on either Windows and Linux and it works perfectly, no errors or warnings, nothing, nyet. However, when I run "R CMD check" on the library, I get an error. I am running R CMD check on linux, and the offending piece of code appears to be:

Dear All: I have a question on using coxph for multiple genes: I have written code to loop through all 22283 genes in the Hgu-133A and apply coxph on survival data. However, I don't know how to work with the result for each gene: survtest<-coxph(Surv(pcc.primary.stg.3.cox[,'fup_interval'],pcc.primary.stg.

Dear experts, I tried to learn about Box-Cox-transformation but found the following thing: When I had to add a constant to make all values of the original variable positive, I found that the lambda estimates (box.cox.powers-function) differed dramatically depending on the specific constant chosen. In addition, the correlation between the transformed variable and the original were not 1 (as I

This works ok: > cox = coxph(surv ~ bucket*(today + accor + both) + activity, data = data) > fit = survfit(cox, newdata=data[1:100,]) but using strata leads to problems: > cox.s = coxph(surv ~ bucket*(today + accor + both) + strata(activity), > data = data) > fit.s = survfit(cox.s, newdata=data[1:100,]) Error in model.frame.default(data = data[1:100, ], formula = ~bucket + :

Dear all R users, I am building a Cox PH model on a small dataset. I am wondering how to measure the predictive power of my cox model? Normally the ROC curve or Gini value are used in logistic regression model. Is there any similar measurement suitable for Cox model? Also if I use C-index statistic to measure the predictive power, is it a time-dependent value (i.e. do I need to calculate it for

The following works, you need to include x=TRUE in the call to coxph. Passing the time and status variables as additional arguments is a matter of personal preference. f.coxph.zph<-function(x, timeVar, statusVar) { cox.fit <- coxph(Surv(timeVar, statusVar) ~ x, na.action = na.exclude, method = "breslow", x=TRUE) fit.zph<-cox.zph(cox.fit) fit.zph$table[,3] } time.cox <-

Hi: Recently, I want to perform a transformation on my data to make it more normal, meanwhile the order statistics is unchanged. So I decided to use a box-cox transformation. below is the qq-plot of the original data http://n4.nabble.com/file/n1011015/start%2Bvalue%2Bproblem%2B02.jpeg Note that the min of my data is -1099, so I add a fix value 1200 to the original sample. I choose the

Hi, It seems there is a problem regarding ControlMaster and scp'ing a file depending on the current working directory: $ cd ~/Personnel $ scp -o ControlMaster=yes cox.jpg host.local: muxserver_listen bind(): No such file or directory lost connection $ scp -o ControlMaster=no cox.jpg host.local: cox.jpg 100% 222KB 222.1KB/s 00:00 $ cd $ scp -o

Hi, I am attempting to evaluate the prediction error of a coxph model that was built after feature selection with glmnet. In the preprocessing stage I used na.omit (dataset) to remove NAs. I reconstructed all my factor variables into binary variables with dummies (using model.matrix) I then used glmnet lasso to fit a cox model and select the best performing features. Then I fit a coxph model

Good morning, Sorry to trouble the list. I have a problem I hope to seek your advice on. Essentially, I am trying to 'validate' a multivariate Cox proportional hazards model built in a training set, by testing it on an external test set. I have performed a survfit using the Cox model to predict survival for the test set, and obtained individual predictions for survival time, with

All, Is is possible for xm create foo.cfg to traverse a symlink to access the foo.img. Here''s an example config and the resulting error.. --- name = "jim" memory = "512" disk = [ ''phy:/dev/VolGroup00/foo,xvda,r'', ''tap:aio:/home/fred/local/jim/build/foo_swap.img,xvdd,w'',

Hello, I'm an MD working in an eye clinic. I'm learning by myself to use R for use in my research works and for implementation in a software project. There are some authors who recomends the use of Cox regression as a substitute for Logistic regression (<a href="http://www.biomedcentral.com/1471-2288/3/21.pdf"> Barros AJD, Hirakata VN. BMCMedical Research Methodology, 2003;

This is a question about the use of the Cox proportional hazards model to analyze event histories. I am looking at the responses of sympathetic nervous system activity to a stimulus. The activity I observe is a burst that can only occur once per heart beat cycle (e.g., a binary count). Typically bursts occur in 60-80% of the heart cycles * sensory stimuli can modify these burst probabilities.

Hi, When I run the following Cox proportional hazards model on the Mayo clinic's PBC data set (given in the "survival" package), the regression coefficients do not agree with the results presented in Table 4.6.3 (p. 195) of Fleming & Harrington's book. library(survival) data(pbc) ans.cox <- coxph(Surv(time, status) ~ log(bili) + log(alb) + age + log(protime) +

Hi, I wrote a function that worked well in R, but not in S-Plus, can anyone suggest a solution? > f.coxph.zph<-function(x) { cox.fit <- coxph(Surv(time.cox, status.cox) ~ x, na.action = na.exclude, method = "breslow") fit.zph<-cox.zph(cox.fit,transform='log') fit.zph$table[,3] } yyy is my data frame that contains survial time, censor status and predictor

Hello I am using box.cox() and I get this error message: Warning message: 'box.cox' is deprecated. Use 'bcPower' instead. See help("Deprecated") and help("car-deprecated"). I went to help but I did not understand the explanation, I am still wondering what is really happening. Thanks /R

Dear Rlist, Using an example in box.cox.powers() help, I have the following warning message. example: library(car) >attach(Prestige) > box.cox.powers(income) Box-Cox Transformation to Normality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) 0.1793 0.1108 1.6179 -7.4062 L.R. test, power = 0: 2.7103 df = 1 p = 0.0997 L.R. test, power = 1: 47.261 df = 1 p = 0

I have been working on fitting Cox model for prediction by using rms package. I want to measure model's calibartion and discrimination. Discrimination was measured by using validate() in rms, Dxy can be transferred to Harrell's c index. But in this way, I cannot get 95%CI of c index. How can I do this in R? And by the way, what value should be in c index to present the model's well?