similar to: weighted Cox proportional hazards regression

Hi, I am wondering if there is a package for doing conditional logistic regression for nested case-control study as described in "Estimation of absolute risk from nested case-control data" by Langholz and Borgan (1997) where Horvitz-Thompson sampling weight (log of (number in the risk set divided by the number sampled)) is used with regression. In SAS Proc Phreg, this is implemented

Dear useRs, I was asked to produce a survival curve like this: http://www.palug.net/Members/jabba/immaginetta.png/view with the cardinality of the riskset at the bottom. I do not like doing it, because it doesn't add any valuable information and because it doesn't discriminate between died and censored. Nevertheless, is there someone able to tell me how to do it? Currently the only

What is 1. par.ests <- optimfit$par 2. fisher <- hessb(negloglik, par.ests, maxvalue=maxima); 3. varcov <- solve(fisher); 4. par.ses <- sqrt(diag(varcov)); Thanks a lot, fit.GEV <- function(maxima) { sigma0 <- sqrt((6. * var(maxima))/pi) mu0 <- mean(maxima) - 0.57722 * sigma0 xi0 <- 0.1 theta <- c(xi0, mu0, sigma0) #10/5/2007: removed assign() for maxima.nl

Dear Specialists in R, May be somebody has experiment in using pakage for the book Quantitative Risk Management by McNeil? This package is writen in R. I have run this package for fitting the data to Nornal Inverse Gaussian distribution and fased with following problem. > Return<-read.csv("data.csv") > Transpose<-t(Return) > fit.NH(Transpose, case="NIG",

for learning purposes and also to help someone, i used roger peng's document to get the mle's of the gamma where the gamma is defined as f(y_i) = (1/gammafunction(shape)) * (scale^shape) * (y_i^(shape-1)) * exp(-scale*y_i) ( i'm defining the scale as lambda rather than 1/lambda. various books define it differently ). i found the likelihood to be n*shape*log(scale) +

Hola. Una duda.... Alguien sabe si hay algo hecho en R para comprobar si un modelo de supervivencia está bien calibrado? (usando las pruebas de Nam-D'Agostino o de Gronnesby & Borgan). Thnks. Un Saludo, _____________________________ Miguel Ángel Rodríguez Muíños Dirección Xeral de Innovación e Xestión da Saúde Pública Consellería de Sanidade Xunta de Galicia http://dxsp.sergas.es

Dear RUsers, I am new to R. I am learning how to use R. I am a PC user and run R on windows. I would appreciate if some one could guide me on a few questions I have: 1) I have 4 cel files (2 replicates for NORM and Disease resp). I have been able to run siggenes on this dataset where I have 4 labels in the class file groupsnhi.cl op-> (0,0,1,1) and my data has been read into datrmanhi after

Dear RUsers, I am new to R. I am learning how to use R. I am a PC user and run R on windows. I would appreciate if some one could guide me on a few questions I have: 1) I have 4 cel files (2 replicates for NORM and Disease resp). I have been able to run siggenes on this dataset where I have 4 labels in the class file groupsnhi.cl op-> (0,0,1,1) and my data has been read into datrmanhi after

Dear all, How can I analyze a life table (e.g. for a cohort of insects) in R? I have 20 insects in 200 cages with two different treatments, whose survival is followed over time, such that, e.g., in one treatment, the number of animals surviving is c(20,18,16,12,10,8,4,0), while in the other treatment the survival is c(20,20,18,18,16,15,15,14) at 8 subsequent time intervals. I would very

Hi guys, interestingly, my problem seems to be solved by writing a FORTRAN wrapper for the Fortran code! (As long as the check doesn't get smarter...). This is the relevant part of my Fortran code: ----------------------------------------------------------- subroutine gmlfun(what, & totevent, totrs, ns, & antrs, antevents, size, & totsize,

Hello. I apologize in advance for the VERY lengthy e-mail. I endeavor to include enough detail. I have a question about survival curves I have been battling off and on for a few months. No one local seems to be able to help, so I turn here. The issue seems to either be how R calculates Kaplan-Meier Plots, or something with the underlying statistic itself that I am misunderstanding. Basically,

Hola Carlos. Muchas gracias. No es exactamente lo que estoy buscando (sería genial ver que alguien tiene un paquete con la prueba de Nam-D'Agostino) pero puedo aprovechar algo de código. Encontré alguna referencia al test de Gronnesby&Borgan en el paquete stcoxgof de Stata, pero estoy torpe para encontrar algo hecho en R (y me extraña que no haya nada) Un saludo, Miguel. De: Carlos

Dear all, I'd like to find the mle esttimates using the mle function mle(negloglik, start = list(), fixed=list(), method="..."). I am using the L-BGFS-B method and I don't supply the gradient function. Is there a way to print the gradients found at the solution value? I am using R-1.9.1 on Windows and on Unix. Thank you in advance, Victoria Landsman. [[alternative

Hi, I want to automatically fit variograms to a large number of different sample data sets, and call the funtion "likfit" (in package geoR) from within a for-loop. "likfit" does again call "optim". After ssuccessfully fitting variograms to some of the data sets, the procedure crashes and I get the error message: Error in optim(par = ini, fn = negloglik.GRF,

Hello, In my reading over the last couple of days, I have read that in order for klee to work with "native" Linux programs, I need to install the uClibc posix support for klee. I am not finding the uClibc source in the llvm svn repository anywhere. Is this still necessary for testing native Linux applications, and if so, where do I get the uClibc source to compile? Thanks in

Sorry for cross-posting, but I realized my question might be more appropriate for r-devel... Thank you, Giovanni ________________________________________ From: R-help <r-help-bounces at r-project.org> on behalf of Giovanni Petris <gpetris at uark.edu> Sent: Tuesday, September 10, 2019 16:44 To: r-help at r-project.org Subject: [R] Calling a LAPACK subroutine from R Hello R-helpers!

I have an R function (about 1000 lines long) that takes more than 20 times as long to run under R Windows 1.9.0 and 1.8.1 than it does under 1.7.1. Profile results indicate that the $<-.data.frame operation is the culprit, but I don't understand exactly what that is (assignment of data frame elements to another variable?), or why it's only a problem under 1.8.1 and 1.9.0. Any advice?

I'm trying to learn how to use namespaces, so I have created a small package with two functions. My NAMESPACE file is importFrom(survival, Surv) export(mlreg.fit, risksets) because 'mlreg.fit' uses 'Surv' from 'survival'. However I get * checking package dependencies ... WARNING Namespace dependencies not required: survival Why? (If I remove 'importFrom' I

Now I try to add some C and Fortan code to my package, so the NAMESPACE file is useDynLib(eha) importFrom(survival, Surv) export(mlreg.fit, risksets) but I get ..... * checking R files for library.dynam ... OK * checking S3 generic/method consistency ... WARNING Error in .try_quietly({ : Error in library(package, lib.loc = lib.loc, character.only = TRUE, verbose = FALSE) :

Hello, coxph does not use any information that are in the dataset between event times (or "death times") , since computation only occurs at event times. For instance, removing observations when there is no event at that time in the whole dataset does not change the results: > set.seed(1) > data <- as.data.frame(cbind(start=c(1:5,1:5,1:4),stop=c(2:6,2:6,2:5),status=c(rep(