Have a look at the survSplit function in the survival package. It looks to me as though you could use survreg with the "weibull" option to achieve what you want. Otherwise, you'll have to rewrite the likelihood in terms of both start and end times.
On 14/09/06, Martin Wagner <[EMAIL PROTECTED]> wrote: > Hello, I am trying to model an intensity function with time-varying covariates. Before, I have successfully defined a log likelihood function for a Power-Law Process (lambda(t)=alpha*beta*t^(beta-1)) with two paramters and no covariates for a repairable systems with failure times (t). This function was maximized with R optim. No problem! But now I want to include a covariate indicating a time-varying value at each failure time t. For constant covariates, the procedure is feasible : leads to following log likelihood funciton: here zi are the covariates which are constant for each unit i under observation. tij are the failure time for failure j of unit i. Do you know how to formulate a log likelihood function for covariates which vary for each tj of each unit i ? Thank you very much Best regards Martin Wagner Berlin University of Technology ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- ================================= David Barron Said Business School University of Oxford Park End Street Oxford OX1 1HP -- ================================= David Barron Said Business School University of Oxford Park End Street Oxford OX1 1HP [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
