Worked analysis of owl data
Hi Jarrord/Ben and list Thanks for this. I have extended the model to a gaussian error with 5 level response variable (IUCN- 1-5) this is a as discrete variable but is an approximation of an underlying continuous spectrum. The reason i am worrying about the residuals ( please follow link to a new picture - https://files.me.com/chrismcowen/0v6ys4) Is that i want to use the fitted values from the model to predict extinction risk ( the response variable) - that way i could include species that don't have a extinction risk, species that weren't in the original model, but for which i have all the necessary life history data. However i am unsure if this is possible with lmer? I hope this makes sense, and thank you for your help Chris
On 12 Aug 2010, at 18:47, Jarrod Hadfield wrote:
Hi Ben/Chris, I agree and would not be unduly worried about the residuals from a binary model. They always look odd if you are used to looking at residuals from a Guassian model, and I'm not sure whether its possible to diagnose problems using them (except complete separation perhaps). Cheers, Jarrod
On 12 Aug 2010, at 16:41, Ben Bolker wrote:
On Thu, Aug 12, 2010 at 5:24 AM, Chris Mcowen <cm744 at st-andrews.ac.uk> wrote:
Hi Ben, I have been working through the above data set I have followed the code to NOT account for random effects in my model, which has worked well - thanks, however as i have a binary response my residual plot shows this https://files.me.com/chrismcowen/i4jxlw Is there a way to Plot predictions and confidence intervals with residuals like this?
Why not? The recipes in the Owls example should work, I think ... with the proviso that (as Jarrod Hadfield said) you have to be very careful in defining what response you are predicting the mean _of_ -- if there are any random effects (other than the intrinsic variability of the binary response) that are non-zero, and if you try to calculate the mean of the predicted response on the original (rather than the link/logit scale), they will affect the prediction of the mean. You seem quite concerned about the odd distributions of the residuals. It's good to be careful, but as far I have seen so far what you are seeing is just the nature of binary residuals. One way to get a handle on what the residuals should look like is to simulate data from a situation reasonably similar to (although often a bit simpler than) what you think is going on with your data, so that you *know* the model is specified correctly, and see what the residuals from the fitted model look like in that case.
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