[R] Discrepant lm() and survreg() standard errors with weighted fits

[Therneau, Terry M., Ph.D.]

  The robust variances are a completely different estimate of standard error.  For linear 
models the robust variance has been rediscovered many times and so has lots of names: the 
White estimate in economics, the Horvitz-Thompson in surveys, working independence 
esitmate in GEE models, infinitesimal jackknife in stat literature, ....  But it is not 
the MLE estimate.
   When the robust estimate was added to survreg (and coxph) I made the decision that IF 
someone was invoking the robust variance, AND they were using weights, that simple case 
weights were unlikely to be what they had.  So I chose to make treat the weights as 
sampling or precision weights, in contraindication to the longer standing behavior of 
coxph/survreg without a robust argument.  Looking back, I probably should have taken one 
step further and changed the routines' behavior globally on the presumption that true case 
weights are vanishingly rare.  They were not uncommon in my computing youth, when computer 
memory of < 64KB was usual (max possible on the PDP-11 architecture).  But one is always 
cautious about non-backwards compatable changes.

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When I use robust=T, I do not understand how survreg treats the
weights as sampling weights and arrives at a different standard error
from lm:

> summary(survreg(Surv(y)~x, dist='gaussian', data=test, weights=rep(2,6), robust=T))$table
                  Value   Std. Err (Naive SE)        z            p
(Intercept)  0.4000000 0.29426260  0.5219013  1.35933 1.740420e-01
x            0.8857143 0.08384353  0.1340119 10.56390 4.380958e-26
Log(scale)  -0.2321528 0.08117684  0.2041241 -2.85984 4.238543e-03