[R] se.fit in predict.glm
(Ted Harding)
Ted.Harding at nessie.mcc.ac.uk
Tue Apr 27 22:08:07 CEST 2004
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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