[R-sig-ME] Interpretation of lme output with correlation structure specification

[Andrew Robinson]

Hi Udita,

Q1 Yes.  The correlation is taken into account in the model.

Q2 I am not sure that I know what you mean by that.  I tend to leave the
value blank and it then gets estimated in the algorithm.

Cheers,

Andrew


On 12 August 2018 at 19:45, Bansal, Udita <udita.bansal17 using imperial.ac.uk>
wrote:

> Dear Andrew,
>
> Thank you for suggesting the book. I went through the relevant parts of
> the book which helped me clarify my third question.
>
> But I still am not clear on phi. What I understood is that it is the
> within group correlation (which is solved by the model?) whose value ranges
> from -1 to 1. What I didn’t understand is as follows:
> Q1: Is any value of phi acceptable since it is the correlation of the
> within group observations which is taken into account by the model?
> Q2: The AR1 parameter estimate (the “value”) I provide while specifying
> the model is calculated based on AR model. How does the phi value relate
> with that? The book did not say much on it.
>
> Any help will be appreciated!
>
> Thanks
> Udita Bansal
>
> From: Andrew Robinson <apro using unimelb.edu.au>
> Date: Saturday, 11 August 2018 at 11:16 PM
> To: "r-sig-mixed-models using r-project.org" <r-sig-mixed-models using r-project.org>,
> "Bansal, Udita" <udita.bansal17 using imperial.ac.uk>
> Subject: Re: [R-sig-ME] Interpretation of lme output with correlation
> structure specification
>
>
> Hi Udita,
>
> You should read the book cited in the package. It’s really worthwhile.
>
> Best wishes,
>
> Andrew
>
> --
> Andrew Robinson
> Director, CEBRA, School of BioSciences
> Reader & Associate Professor in Applied Statistics Tel: (+61) 0403 138 955
> School of Mathematics and Statistics Fax: (+61) 03 8344 4599
> University of Melbourne, VIC 3010 Australia
> Email: apro using unimelb.edu.au
> Website: http://cebra.unimelb.edu.au/
> On 12 Aug 2018, 7:34 AM +1000, Bansal, Udita <
> udita.bansal17 using imperial.ac.uk>, wrote:
>
> Hi all,
>
> I was modeling the laying date of bird nests against moving averages of
> weather variables for several years of data. I used Durbin-Watson test and
> found considerable amount of autocorrelation in the residuals of simple
> linear and mixed effect models (with month as a random factor). So, I
> decided to run lme models with correlation structure specified. When I
> compare the AIC of the models with and without the correlation structure, I
> find that the models with the correlation structure are better.
> Question 1.: How can I interpret the phi (parameter estimate for
> correlation structure) value in the model output?
> Question 2.: Does the interpretation of phi affect the interpretation of
> the random effect?
> Question 3.: How can I interpret the random effect (since this is
> different from what lmer output shows which I am used to of)?
>
> An example output is as below:
>
> Random effects:
> Formula: ~1 | month
> (Intercept) Residual
> StdDev: 12.53908 5.009051
>
> Correlation Structure: AR(1)
> Formula: ~1 | month
> Parameter estimate(s):
> Phi
> 0.324984
>
> I could not find much on the interpretation for these online. Any help
> will be much appreciated.
>
> Thanks
> Udita Bansal
>
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-- 
Andrew Robinson
Director, CEBRA, School of BioSciences
Reader & Associate Professor in Applied Statistics  Tel: (+61) 0403 138 955
School of Mathematics and Statistics                        Fax: (+61) 03
8344 4599
University of Melbourne, VIC 3010 Australia
Email: apro using unimelb.edu.au
Website: http://www.ms.unimelb.edu.au/~andrewpr

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