similar to: Error in Surv(time, status) : Time variable is not numeric

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

Hi, when i run the following code i get this massege: "The instruction at 0x######## reference memory at 0x#######, the memory cannot be "read". and then i have to close R. what is the problem and how can i solve it? thanks in advance Avi my code # frailtypack library(frailtypack) cgd.ag <- read.csv("C:/rfiles/RE/cgd.csv") cgd.nfm <-frailtyPenal(Surv(TStart,

Hello, I have a cohort with approx 1,200 patients at the ages of 30-65 that had their first myocardial infarction during 1992: · They were in a follow up until 2005. · About 400 of them died during this period of time (right censored) · Each one of them had up to 4 mi recurrent events. I am using the semi-parametric model in order to assess the relationship of

Dear List, In documents (Therneau, 2003 : On mixed-effect cox models, ...), as far as I came to know, coxme penalize the partial likelihood (Ripatti, Palmgren, 2000) where as frailtyPenal (in frailtypack package) uses the penalized the full likelihood approach (Rondeau et al, 2003). How, then, coxme and coxph(...frailty(id, dist='gauss')) differs? Just the coding algorithm, or in

Dear R-listers, I have a very strange problem. I made a package (under Windows and Linux). The package passed the R CMD Check without problem. Then, I installed the package and executed a function which calls to a 'dll' mod<-frailtyPenal(Surv(time,status)~sex+age+cluster(id), + n.knots=8,kappa1=10000,data=kidney) mod Call: frailtyPenal(formula = Surv(time, status)

Hi everyone I am attempting to estimate a model with a frailty effect distributed as a lognormal variable.I am using the following code: frailtyPenal(formula, data, ..., RandDist = "LogN") I get the following error message: Error in frailtyPenal(Surv(,) ~ + , : unused argument(s) (RandDist = "") What can I do? Thanks for the help [[alternative HTML version deleted]]

We're involved in a project that the requirements could be satisfied with both samba3 and 4. Anyway I am testing what can be done with Samba4 and after following the tutorial published in the official wiki, I was able to create my test domain, and join WinXP and Win7 machines to it without a problem. I still need to test the GPO functionality, and some other stuff, but before continuing with

  To   R group Help Desk     I am a user of R software.  I am facing a problem while using "frailtyPenal" command in R.2.11.1. When I use these command, R closes completely without any prior alert message. Can I know what would be the reason? My data size is 7050 records with atleast 25 variables.   Looking forward to hear from you.   Truely Kalaivani M.Kalaivani, M.Sc. Scientist-I

Hi everybody, I'm trying to create a numerical data frame on which to perform PRCC. So far I have created a data frame that consists of function/vector output that displays in numerical form, but when I try and run PRCC (from epiR package) I get the following error message: "Error in solve.default(C) : Lapack routine dgesv: system is exactly singular" It appears this is because

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

Dear R users, We are pleased to tell you that "FRAILTYPACK" has been updated. "FRAILTYPACK" stands now for general frailty models estimated with a semi-parametrical penalized likelihood, but also with a parametrical approach. In case of comments/corrections/remarks/suggestions -- which are very welcome --please contact the maintainer directly. Kind regards, The

Dear R users, We are pleased to tell you that "FRAILTYPACK" has been updated. "FRAILTYPACK" stands now for general frailty models estimated with a semi-parametrical penalized likelihood, but also with a parametrical approach. In case of comments/corrections/remarks/suggestions -- which are very welcome --please contact the maintainer directly. Kind regards, The

Hello I would like to compare the results obtained with a classical non parametric proportionnal hazard model with a parametric proportionnal hazard model using a Weibull. How can we obtain the equivalence of the parameters using coxph(non parametric model) and survreg(parametric model) ? Thanks Virginie

A new version of the package FRAILTYPACK is now available on CRAN. -- possibility to fit now a Shared and a Joint Frailty model with a log-normal distribution for the random effects. -- possibility to deal with interval-censored data (for a shared frailty model) -- possibility to fit a joint frailty model for clustered data For more details see the corresponding NEWS files in the pkgs. We are

A new version of the package FRAILTYPACK is now available on CRAN. -- possibility to fit now a Shared and a Joint Frailty model with a log-normal distribution for the random effects. -- possibility to deal with interval-censored data (for a shared frailty model) -- possibility to fit a joint frailty model for clustered data For more details see the corresponding NEWS files in the pkgs. We are

 Dear R-users,   I  have two questions regarding validation and calibration of Survival regression models.   1.  I am trying to calibrate and validate a cox model using val.surv. here is my code:  f.1<-cph(Surv(time,event)~age, x=T, y=T, data=train)  test1<-test[,"age"]  val.surv(f.1, newdata=data.frame(test1), u=10)    but I get an error message:    Error in val.surv(f.1, newdata

R-Listers: I am doing a quasi-maximum likelihood estimation and I get a "subscript out of bound" error message, Typically I would think this means that a subscript used in the function is literally out of bounds however I don't think this is the case. All I change in the code is a constant, that is hard-wired in (not data dependent and not parameter dependent), furthermore,

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

Dear R-help, I am trying to estimate a Cox model with nested effects, or better h(t,v,w)=v*w*h0(t)*exp(B'x) where h(t,v,w) is the individual hazard function w and v are both frailty terms (gamma or normal distributed) I have 12 clusters and for each one of them I would like to associate a realization of v, while w is a random effect for the whole population. At the population level