[R-sig-ME] Worked analysis of owl data

[Chris Mcowen]

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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