R help - Feb 2010 - linear predictors and survreg function

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

Note that the first set of coefficients minus the second set of coefficients
is constant (12.78262)
-- 
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