similar to: Survival analysis with events at t=0

I using oldlogspline (from logspline package) to model data distributions, and having a problem. My data are search area sizes. They are based on circular search radii from random points to the nearest edge of the nearest grass tussock. Search area sizes are distributed from 0 (the random point intercepts a tussock) and upwards (as points are further from any tussocks). The density of all my

I'm wondering if anyone is aware of a way to take what is visible in the R graphics window, pixelate it, and express that pixellation in a numeric matrix. For example, I am using the following command to create a black (present) and white (absent) spatial simulation of grass tussock distribution. > symbols(x=runif(100,-.5,1.5),y=runif(100,-.5,1.5),circles=runif(100)/30,

Dear All, I like to know how to simulate survival data using median (or mean) survival time. Any help will be greatly appreciated. Best wishes, Kere Kerenaftali Klein PhD| Biostatistician | Queensland Clinical Trials & Biostatistics Centre The University of Queensland | School of Population Health | Building 33, Level 1| Princess Alexandra Hospital |Ipswich Road | Woolloongabba QLD 4102 |

Dear R users! I want to process a maximum likelihood estimation for a parametric regression survival time model with multiple events per subject. the STATA command for this survival regression is: use survreg stset failure(exercise), id(optionid) local regressors itm posret negret streg `regressors', distribution(weibull) explanation: stset declares data to be survival-time data; exercise

I am trying to generate two dimensional random coordinates. For randomly distributed data I have simply used >xy<-cbind(runif(100),runif(100)) However I also want to generate coordinates that are more uniformly distributed, and coordinates that are more contagiously distributed than the above. Can anyone make any suggestions Thanks. Dr Terry Beutel Rangeland Scientist Animal

Hi All, I've been trawling through the documentation and listserv archives on this topic -- but as yet have not found a solution. I'm sure this is pretty simple with R, but I cannot work out how without resorting to ugly nested loops. As far as I can tell, grep, match, and %in% are not the correct tools. Question: given these vectors -- patrn <- c(1,2,3,4) exmpl <-

Dear All, I would like to fit some parametric survival models using left truncated, right censored data in R. However I am having problems finding a function to fit parametric survival models which can handle left truncated data. I have tested both the survreg function in package survival: fit1 <- survreg(Surv(start, stop, status) ~ X + Y + Z, data=data1) and the psm function in package

Hi, I was trying to fit a parametric survival model with Weibull distribution on counting process type of data (NOT interval censor data), but the survreg(Surv(T1,T2,event)~x,data,dist="weibull") did not seem to work. Anyone can help me with that? Thanks, Rachel Memorial Sloan-Kettering Cancer Center -- View this message in context:

Hi there, I need some help to figure out what is the proper model in survival analysis for my data. Subjects were randomized to 3 treatments in trial 1, some of them experience the event during the trial; After period of time those subjects were randomized to 3 treatments again in trial 2, but different from what they got in 1st trial, some of them experience the event during the 2nd trial (I

I am confused on how the log-likelihood is calculated in a parametric survival problem with frailty. I see a contradiction in the frailty() help file vs. the source code of frailty.gamma(), frailty.gaussian() and frailty.t(). The function frailty.gaussian() appears to calculate the penalty as the negative log-density of independent Gaussian variables, as one would expect: >

Hi, I am looking to do some quantitative genetic analyses using animal models and was wondering if someone could suggest an appropriate package in R. It would help if it was similar to the ASReml genetic analyses software. Thanks, Deepa Senapathi Deepa Senapathi Centre for Agri-Environmental Research (CAER) School of Agriculture, Policy & Developement. University of Reading

On Tue, 6 May 2003, Jerome Asselin wrote: > > I am confused on how the log-likelihood is calculated in a parametric > survival problem with frailty. I see a contradiction in the frailty() help > file vs. the source code of frailty.gamma(), frailty.gaussian() and > frailty.t(). > > The function frailty.gaussian() appears to calculate the penalty as the > negative

Hi, I compare results of a simple two-level poisson estimated using lmer and those estimated using MLwiN and Stata (v.9). In R, I trype: ------------------------------------------------------------------------------------------- m2 <- lmer(.D ~ offset(log(.Y)) + (1|pcid2) + educy + agri, male, poisson) -------------------------------------------------------------------------------------------

Dear r-users, Does anyone know how to draw time-dependent survival curves? Example: Event outcome: CHD Time-dependent covariate: NSAID use, which changes over time for each subject I'm interested in survival curves stratified by NSAID use. I'd like to implement Simon & Makuch (1984) method. Is there a R package/function to draw this graph?

Hi, I would like to fit parametric survival models to time-to-event data that are left truncated. I have checked the help page for survreg and looked in the R-help archive, and it appears that the R function survreg from the survival library (version 2.16) should allow me to take account of left truncation. However, when I try the command

Dear all, I'm looking for someone that help me to write an R function to simulate survival data under complex situations, namely time-varying hazard ratio, marginal distribution of survival times and covariates. The algorithm is described in the reference below and it should be not very difficult to implement it. However I tried but without success....;-( Below there the code that I used; it

Hi experts, http://r.789695.n4.nabble.com/file/n4034318/Parametric_survival_analysis_2nd-order_efffect.JPG Parametric_survival_analysis_2nd-order_efffect.JPG As we know a normal survival regression is the equation (1) Well, I'ld like to modify it to be 2nd-order interaction model as shown in equation(2) Question: Assume a and z is two covariates. x = dummy variable (1 or 0) z = factors

Dear R users, I'd like to announce the RcmdrPlugin.survival package, which has just been made available on CRAN. The package provides an R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests,

Dear R users, I'd like to announce the RcmdrPlugin.survival package, which has just been made available on CRAN. The package provides an R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests,

I will have data in the following form: Time resp type stim type 300 a A 200 b A 155 a B 250 b B 80 c A 1000 d B ... c is left censored observation; d is right censored This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of Survival Data under the name