I'm trying to calculate a CI for predictions from a Poisson GLM object egg.glm.
Browse[2]> aa <- as.data.frame(predict(egg.glm, newdat, type = "response",
se.fit = TRUE)[-3])
Browse[2]> bb <- as.data.frame(predict(egg.glm, newdat, se.fit = TRUE)[-3])
Browse[2]> aa
fit se.fit
1 6.144212e-07 0.0005114257
2 2.452632e+01 5.4657657443
3 1.440000e+01 2.5817126393
4 4.389796e+01 4.5533997800
5 3.820455e+01 4.4827326393
6 6.226667e+01 5.6589154967
Browse[2]> bb
fit se.fit
1 -14.302585 832.36979026
2 3.199747 0.22285311
3 2.667228 0.17928560
4 3.781868 0.10372691
5 3.642954 0.11733506
6 4.131426 0.09088194
Browse[2]>
bb$fit is clearly log of aa$fit but just what is se.fit? How do I use
it to get a CI which is calculated on the log scale? The first one is
a bit messy since it is entirely from zeros. Should I remove those or
would that be unnecessary?
TIA
--
~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.
___ Patrick Connolly
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