[R] se.fit in predict.glm

[(Ted Harding)]

Hi Folks,

I'm seeking confirmation of something which is probably true
but which I have not managed to find in the documentation.

I have a binary response y={0.1} and a variable x and have
fitted a probit response to the data with

  f <- glm( y~x, family=binomial(link=probit) )

and then, with a specified set of x-value X I have used the
predict.glm function as

  p <- predict( f, X, type="response", se.fit=TRUE )

obtaining, as described in ?predict.glm, a list p with components

  p$fit  the fitted values (of P[y=1]) at the value of X

  p$se.fit

The documentation does not say definitely what p$se.fit is,
only calling it "Estimated standard errors". I *believe*
this means, at each value of X, the SE in the estimation
of P[y=1] taking account of the joint uncertainty in the
estimation of 'a' and 'b' in the relation

  probit(P) = a + b*X

Can someone confirm that this really is so?

With thanks,
Ted.


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E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk>
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Date: 27-Apr-04                                       Time: 21:08:07
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