Johannes Huesing <johan...@huesing.name> [Mon, Jan 10, 2011 at 09:26:37PM CET]:
> I am trying to model survival data with a Weibull distribution
>
> using survreg. Units are clustered two apiece, sometimes receiving
>
> the same treatment and sometimes opposing treatment.
>
>
>
> Residual and predict methods are not carried out on the survreg
>
> object, although the component linear.predictors exists for the
>
> survreg object. Looking at the code I see that the residual method
>
> refuses to run if any of the components of the pterms vector is
>
> equal to 2.
>
>
>
> When taking the logarithm of the data and trying a linear mixed
>
> model, residuals and predicted values are produced just fine.
>
>
>
> So what is pterms and why is it that, when any component of it
>
> is 2, residuals are not allowed to be displayed?
I am using R 2.10.1, and here is some example code:
anzpat <- 32
device <- rep(unlist(expand.grid(list(c("C", "Z"), c("C", "Z")))),
anzpat / 4)
bleedtimes <- exp(rnorm(anzpat * 2, 0, .3) + as.numeric(device) * log(3))
pat <- as.factor(rep(1:anzpat, each=2))
library(survival)
survmod <- survreg(Surv(sapply(bleedtimes, min, 10), bleedtimes < 10) ~ device
+ frailty.gaussian(pat))
residuals(survmod)
### Fehler in residuals.survreg.penal(survmod) :
### Residualss not available for sparse models
survmod$pterms
### (Intercept) device frailty.gaussian(pat)
### 0 0 2
--
Johannes Hüsing There is something fascinating about science.
One gets such wholesale returns of conjecture
mailto:johan...@huesing.name from such a trifling investment of fact.
http://derwisch.wikidot.com (Mark Twain, "Life on the Mississippi")
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