similar to: logLik calculation in gls (nlme)

-------- Original Message -------- Subject: Re: How to obtain nonparametric baseline hazard estimates in the gamma frailty model? Date: Mon, 04 Nov 2013 17:27:04 -0600 From: Terry Therneau <therneau.terry at mayo.edu> To: Y <yuhanusa at gmail.com> The cumulative hazard is just -log(sfit$surv). The hazard is essentially a density estimate, and that is much harder. You'll notice

Dear all, I am using glmnet (Coxnet) for building a Cox Model and to make actual prediction, i.e. to estimate the survival function S(t,Xn) for a new subject Xn. If I am not mistaken, glmnet (coxnet) returns beta, beta*X and exp(beta*X), which on its own cannot generate S(t,Xn). We miss baseline survival function So(t). Below is my code which takes beta coefficients from glmnet and creates coxph

Dear R-help, I am using R version 3.4.0 within Windows, and survival 2.41-3. I have fit a Prentice Williams and Peterson-Counting Process model to my data as shown below. This is basically an extension of the Cox model for interval censored data. My dataset, bdat5 can be found here: https://drive.google.com/open?id=1sQSBEe1uBzh_gYbcj4P5Kuephvalc3gh cfitcp2 <-

Dear all, I am confused with the behaviour of survfit with newdata option. I am using the latest version R-2-15-0. In the simple example below I am building a coxph model on 90 patients and trying to predict 10 patients. Unfortunately the survival curve at the end is for 90 patients. Could somebody please from the survival package confirm that this behaviour is as expected or not - because I

Hi everyone. There's been several threads on baseline hazard in Cox model but I think they were all on cumulative baseline hazard, for instance http://tolstoy.newcastle.edu.au/R/help/01a/0464.html http://tolstoy.newcastle.edu.au/R/help/01a/0436.html "basehaz" in package survival seems to do a cumulative hazard. extract from the basehaz function: sfit <- survfit(fit) H

Hi, I have a problem in getting step function work. I am getting the following error: > fit1 <- lm(Response~1) > fmla <- as.formula(paste(" ~ ",paste(colnames,collapse="+"))) > sfit <- step(fit1,scope=list(upper= fmla,lower= ~1),k=log(nrow(dat))) Start: AIC= -1646.66 Response ~ 1 Error in "contrasts<-"(`*tmp*`, value =

Dear All, I have a question regarding the output of survfit() when I supply a Cox model. Lets say for example: library(survival) fit <- coxph(Surv(time, status == 2) ~ factor(spiders), data = pbc) fit # HR for spiders is significant newdata <- data.frame(spiders = factor(0:1)) sf <- survfit(fit, newdata = newdata) sum.sf <- summary(sfit, times = c(2000, 2500, 3000)) # survival