Dear list, I would like to simulate individual survival times from a model that has been fitted using the survreg procedure (library survival). Output shown below. My plan is to extract the shape and scale arguments for use with rweibull() since my error terms are assumed to be Weibull, but it does not make any sense. The mean survival time is easy to predict, but I would like to simulate individual survival times. I am probably missing something completely obvious. Any hints or advice are appreciated. Thanks Sixten> summary(mod1)Call: survreg(formula = Surv(tid, study$first.event.death) ~ regim + age + stadium2, data = study, dist = "weibull") Value Std. Error z p (Intercept) 11.6005 0.7539 15.387 2.01e-53 regimposto -0.1350 0.1558 -0.867 3.86e-01 age -0.0362 0.0102 -3.533 4.11e-04 stadium2ii -0.0526 0.2794 -0.188 8.51e-01 Log(scale) -0.5148 0.1116 -4.615 3.93e-06 Scale= 0.598 Weibull distribution Loglik(model)= -680.7 Loglik(intercept only)= -689.2 Chisq= 16.87 on 3 degrees of freedom, p= 0.00075 Number of Newton-Raphson Iterations: 8 n=1183 (4 observations deleted due to missing)> version_ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.1 year 2003 month 11 day 21 language R>
Please check the documentation, e.g., Venables and Ripley (2002) Modern Applied Statistics with S, p. 360. The Weibull shape parameter is the reciprocal of the scale parameter 0.598 in your printout, so shape = 1/0.598 = 1.672; see also Meeker & Escobar (1998) Statistical Methods for Reliability Data (Wiley). Does this answer the question? You can get coefficients with coef(mod1). Also, have you looked at attributes(summary(mod1))? If "mod1" follows the old S3 standard, attributes may give you a list of names you can access via summary(mod1)$whateverthenameis (or via summary(mod1)[["whateverthenameis"]]). If "mod1" follows the new S4 standard, then getSlots(mod1) and getSlots(summary(mod1)) will give you the names of the slots and their classes, which can then be accessed via mod1 at nameofslotofinterest. Sorry, I don't have time to construct an example myself, but I've done this kind of thing many times. hope this helps. spencer graves Sixten Borg wrote:>Dear list, > >I would like to simulate individual survival times from a model that has been fitted using the survreg procedure (library survival). Output shown below. > >My plan is to extract the shape and scale arguments for use with rweibull() since my error terms are assumed to be Weibull, but it does not make any sense. The mean survival time is easy to predict, but I would like to simulate individual survival times. > >I am probably missing something completely obvious. Any hints or advice are appreciated. > >Thanks >Sixten > > > >>summary(mod1) >> >> > >Call: >survreg(formula = Surv(tid, study$first.event.death) ~ regim + > age + stadium2, data = study, dist = "weibull") > Value Std. Error z p >(Intercept) 11.6005 0.7539 15.387 2.01e-53 >regimposto -0.1350 0.1558 -0.867 3.86e-01 >age -0.0362 0.0102 -3.533 4.11e-04 >stadium2ii -0.0526 0.2794 -0.188 8.51e-01 >Log(scale) -0.5148 0.1116 -4.615 3.93e-06 > >Scale= 0.598 > >Weibull distribution >Loglik(model)= -680.7 Loglik(intercept only)= -689.2 > Chisq= 16.87 on 3 degrees of freedom, p= 0.00075 >Number of Newton-Raphson Iterations: 8 >n=1183 (4 observations deleted due to missing) > > > >>version >> >> > _ >platform i386-pc-mingw32 >arch i386 >os mingw32 >system i386, mingw32 >status >major 1 >minor 8.1 >year 2003 >month 11 >day 21 >language R > > > >______________________________________________ >R-help at stat.math.ethz.ch mailing list >https://www.stat.math.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > >
On Wed, 28 Jul 2004, Sixten Borg wrote:> Dear list, > > I would like to simulate individual survival times from a model that has > been fitted using the survreg procedure (library survival). Output shown > below. > > My plan is to extract the shape and scale arguments for use with > rweibull() since my error terms are assumed to be Weibull, but it does > not make any sense. The mean survival time is easy to predict, but I > would like to simulate individual survival times. >It's parametrized differently from rweibull. The linear predictor for the model is the logarithm of the scale, and the scale parameter from the model is the reciprocal of the shape. As with the gamma family you always need to check the parametrisation for weibull distributions. -thomas> I am probably missing something completely obvious. Any hints or advice > are appreciated. > > Thanks > Sixten > > > summary(mod1) > > Call: > survreg(formula = Surv(tid, study$first.event.death) ~ regim + > age + stadium2, data = study, dist = "weibull") > Value Std. Error z p > (Intercept) 11.6005 0.7539 15.387 2.01e-53 > regimposto -0.1350 0.1558 -0.867 3.86e-01 > age -0.0362 0.0102 -3.533 4.11e-04 > stadium2ii -0.0526 0.2794 -0.188 8.51e-01 > Log(scale) -0.5148 0.1116 -4.615 3.93e-06 > > Scale= 0.598 > > Weibull distribution > Loglik(model)= -680.7 Loglik(intercept only)= -689.2 > Chisq= 16.87 on 3 degrees of freedom, p= 0.00075 > Number of Newton-Raphson Iterations: 8 > n=1183 (4 observations deleted due to missing) > > > version > _ > platform i386-pc-mingw32 > arch i386 > os mingw32 > system i386, mingw32 > status > major 1 > minor 8.1 > year 2003 > month 11 > day 21 > language R > > > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >Thomas Lumley Assoc. Professor, Biostatistics tlumley at u.washington.edu University of Washington, Seattle