Can anyone help me calculating CIs from a GAM analysis?
I have calculated a GAM fit (m3) and the associated std errors using
predict.gam
I assume that the 95% CI around each fit value would be 1.96
times the se. But when I do this both on the original and a test
dataset, I find the CI's only encompass about half of the true response
values. I have tried this using predict.gam using both type="response"
and type="link" and get nearly the same result. What am I doing wrong?
Here is the code I am using to get the 95% CIs.
se3=predict.gam(m3,dat, type="response", se.fit=TRUE)
upr <- se3$fit + (1.96 * se3$se.fit)
upr <- m3$family$linkinv(upr)
lwr <- se3$fit - (1.96 * se3$se.fit)
lwr <- m3$family$linkinv(lwr)
CI95=upr-lwr
CI95=CI95/2
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