Hi,
I calculated the linear predictors derived from weibull model using ovarian
data sets. I calculated the linear predictors as the sum of covariates weighted
by the weibull coefficients and compared to the linear predictors generated by
survreg function. Why are they different? note that the first element of
coefficients vector is intercept was excluded in my calculation.
Look forward to your reply,
Carol
--------------------------------------------------
data(ovarian)
library(survival)
survreg.obj = survreg(Surv(ovarian[,1],ovarian[,2])~age +resid.ds +rx
+ecog.ps,ovarian, dist = "weibull", scale = 1)
> survreg.obj$linear.predictors
[1] 5.298074 5.108976 5.558852 7.584172 7.221841 7.202655 7.019320 6.764081
[9] 6.011550 7.939097 7.174129 8.634805 6.783737 7.261585 8.955989 8.366687
[17] 7.970807 8.489844 8.302639 8.385361 7.553247 4.855690 7.851908 7.235689
[25] 6.616655 7.917497
*******************
lp = survreg.obj$coefficients[2:5]%*%t(ovarian[,3:6])
> lp
1 2 3 4 5 6 7
[1,] -7.484549 -7.673647 -7.223771 -5.198451 -5.560782 -5.579968 -5.763303
8 9 10 11 12 13 14
[1,] -6.018542 -6.771073 -4.843526 -5.608495 -4.147818 -5.998886 -5.521038
15 16 17 18 19 20 21
[1,] -3.826634 -4.415936 -4.811816 -4.292779 -4.479984 -4.397262 -5.229376
22 23 24 25 26
[1,] -7.926933 -4.930715 -5.546934 -6.165968 -4.865126
______________________________________________
R-help@r-project.org 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.
