Regression slope confidence interval

Why not use vcov() and the normal approximation ?

 > ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
 > trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
 > group <- gl(2,10,20, labels=c("Ctl","Trt"))
 > weight <- c(ctl, trt)
 > lm.D9 <- lm(weight ~ group)
 >
 > cbind(estimate = coef(lm.D9),
+       lower = coef(lm.D9) - 1.96 * diag(vcov(lm.D9)),
+       upper = coef(lm.D9) + 1.96 * diag(vcov(lm.D9)))
             estimate      lower      upper
(Intercept)    5.032  4.9369482  5.1270518
groupTrt      -0.371 -0.5611037 -0.1808963

To address your needs, it might also be possible to write a method for 
the generic of intervals() in package nlme.

Best,

Renaud

Christian Hennig a ??crit :

Hi list,

is there any direct way to obtain confidence intervals for the regression
slope from lm, predict.lm or the like?
(If not, is there any reason? This is also missing in some other statistics
softwares, and I thought this would be quite a standard application.)
I know that it's easy to implement but it's for
explanation to people who faint if they have to do their own
programming...

Christian

*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche

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