Well, it’s not! However, the main reason why people mess up computing confidence intervals for a GLM is that they do all the calculations on the response scale. Why is plus/minus two standard errors wrong? If I had a dollar (even a Canadian one) for every time I’ve seen someone present graphs of estimated abundance of some species where the confidence interval includes negative abundances, I’d be rich! Here, following the rule of “if I’m asked more than once I should write a blog post about it!” I’m going to show a simple way to correctly compute a confidence interval for a GLM or a related model. Unfortunately this only really works like this for a linear model. If you remember a little bit of theory from your stats classes, you may recall that such an interval can be produced by adding to and subtracting from the fitted values 2 times their standard error. In general this is done using confidence intervals with typically 95% converage. You’ve estimated a GLM or a related model (GLMM, GAM, etc.) for your latest paper and, like a good researcher, you want to visualise the model and show the uncertainty in it.
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