Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111
> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-
> models-bounces at r-project.org] On Behalf Of Chris Mcowen
> Sent: Friday, August 13, 2010 1:27 AM
> To: Ben Bolker; Jarrod Hadfield
> Cc: R-mixed models mailing list
> Subject: Re: [R-sig-ME] 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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