similar to: Evaluation without using the parent frame

Duncan, I agree completely with "don't use attach"; if I could get all the users of the survival package to agree as well the problem in question would go away :-) I'm thinking about ways to add more effective error surveillance. Your suggestion was not horribly complex and I'll look into it further. My first foray failed because what I want (I think) is the

I?m trying to set up proportional hazard model that is stratified with respect to covariate 1 and has an interaction between covariate 1 and another variable, covariate 2. Both variables are categorical. In the following, I try to illustrate the two problems that I?ve encountered, using the lung dataset. The first problem is the warning: To me, it seems that there are too many dummies

First, here is your message as it appears on R-help. On 10/14/2012 05:00 AM, r-help-request@r-project.org wrote: > I?m trying to set up proportional hazard model that is stratified with > respect to covariate 1 and has an interaction between covariate 1 and > another variable, covariate 2. Both variables are categorical. In the > following, I try to illustrate the two problems that

Hello R helpers: *My first message didn't pass trough filter so here it's again* I would like to obtain probability of an event for one single patient as a function of time (from survfit.coxph) object, as I want to find what is the probability of an event say at 1 month and what is the probability of an event at 80 months and compare. So I tried the following but it fails miserably. I

I was looking at how the model.frame method for lm works and comparing it to my own for coxph. The big difference is that I try to retain xlevels and predvars information for a new model frame, and lm does not. I use a call to model.frame in predict.coxph, which is why I went that route, but never noted the difference till now (preparing for my course in Nashville). Could someone shed light

I am confused when trying the function survfit. my question is: what does the survival curve given by plot.survfit mean? is it the survival curve with different covariates at different points? or just the baseline survival curve? for example, I run the following code and get the survival curve #### library(survival) fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)

Dear R users, My apologies for the simple question, as I'm starting to learn the concepts behind the Cox PH model. I was just experimenting with the survival and rms packages for this. I'm simply trying to obtain the expected survival time (as opposed to the probability of survival at a given time t). I can't seem to find an option from the "type" argument in the predict

I've narrowed my scope problems with predict.coxph further. Here is a condensed example: fcall3 <- as.formula("time ~ age") dfun3 <- function(dcall) { fit <- lm(dcall, data=lung, model=FALSE) model.frame(fit) } dfun3(fcall3) The final call fails: it can't find 'dcall'. The relevant code in model.frame.lm is: env <- environment(formula$terms)

I have a debugging environment for the survival package, perhaps unique to me, but I find it works very well. To wit, a separate directory with copies of the source code but none of the package accuements of DESCRIPTION, NAMESPACE, etc. This separate space does NOT contain a copy of src/init.c Within this I use R --vanilla, attach my .RData file, survival.so file, and away we go. That is,

Since I read the list in digest form (and was out ill yesterday) I'm late to the discussion. There are 3 steps for predicting survival, using a Cox model: 1. Fit the data fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung) The biggest question to answer here is what covariates you wish to base the prediction on. There is the usual tradeoff between too few (leave out something

Dear all, I have been trying to plot hazard function in R for survival data, but in vain. Can anybody help me out in plotting hazard function in R? Dr Suman Kumar -- View this message in context: http://r.789695.n4.nabble.com/how-to-plot-hazard-function-for-coxph-model-tp4637953.html Sent from the R help mailing list archive at Nabble.com.

Dear all - I have followed the thread the reply to which was lead by Thomas Lumley about using pweibull to generate fitted survival curves for survreg models. http://tolstoy.newcastle.edu.au/R/help/04/11/7766.html Using the lung data set, data(lung) lung.wbs <- survreg( Surv(time, status)~ 1, data=lung, dist='weibull') curve(pweibull(x, scale=exp(coef(lung.wbs)),

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

Dear List, Must be a silly question, but I was wondering whether there is a direct way of calculating "standard error of a HR or exp(coef)" from coxph objects x <- coxph(Surv(time, status) ~ age + inst, lung)> x coef exp(coef) se(coef) z page 0.0190 1.02 0.00925 2.06 0.04inst -0.0104 0.99 0.01028 -1.01 0.31 cheers, Ehsan [[alternative HTML

Hello, My question concerns model selection, stepAIC(), add1(), and coxph(). In Venables and Ripley (3rd Ed) pp389-390 there is an example of using stepAIC() for the automated selection of a coxph model for VA lung cancer data. A statistics question: Can partial likelihoods be interpreted in the same manner as likelihoods with respect to information based criterion and likelihood ratio tests?

I recently had a long argument wrt the survival package, namely that the following code didn't do what they expected, and so they reported it as a bug ? survival::coxph( survival::Surv(time, status) ~ age + sex + survival::strata(inst), data=lung) a. The Google R style guide? recommends that one put :: everywhere b. This breaks the recognition of cluster as a "special" in the

R Folk: Please forgive what I'm sure is a fairly na?ve question; I hope it's clear. A colleague and I have been doing a really simple one-off survival analysis, but this is an area with which we are not very familiar, we just happen to have gathered some data that needs this type of analysis. We've done quite a bit of reading, but answers escape us, even though the question below

Dear R users, Please can you help me with a relatively straightforward problem that I am struggling with? I am simply trying to plot a baseline survivor and hazard function for a simple data set of lung cancer survival where `futime' is follow up time in months and status is 1=dead and 0=alive. Using the survival package: lung.wbs <- survreg( Surv(futime, status)~ 1, data=lung,

I want use survfit() and basehaz() inside a function, but it doesn't work. Could you take a look at this problem. Thanks for your help. Following is my codes: library(survival) n <- 50 # total sample size nclust <- 5 # number of clusters clusters <- rep(1:nclust,each=n/nclust) beta0 <- c(1,2) set.seed(13) #generate phmm data set Z <- cbind(Z1=sample(0:1,n,replace=TRUE),

Hello: I would like to obtain probability of an event for one single patient as a function of time (from survfit.coxph) object, as I want to find what is the probability of an event say at 1 month and what is the probability of an event at 80 months and compare. So I tried the following but it fails miserably. I looked at some old posts but could not figure out the solution. Here's what I did