[R-sig-ME] Interpretation of lme output with correlation structure specification
Andrew Robinson
A@Robin@on @ending from m@@unimelb@edu@@u
Mon Aug 13 13:45:51 CEST 2018
What you write seems reasonable. I can't say more because I don't know how
you fit the model or anything about your data.
Andrew
On 13 August 2018 at 17:56, Bansal, Udita <udita.bansal17 using imperial.ac.uk>
wrote:
> Hi Andrew,
>
>
>
> Thanks for your response.
>
>
>
> I had just one more question. I was using a nested random effect and the
> output looks like follows:
>
>
>
> Random effects:
>
> Formula: ~1 | year
>
> (Intercept)
>
> StdDev: 0.001158148
>
>
>
> Formula: ~1 | month %in% year
>
> (Intercept) Residual
>
> StdDev: 7.551615 3.77298
>
>
>
> From an example on non-nested random effect in the book, I understood that
> (Intercept) is the between group variance explained by the random effect
> and Residual value gives the within-group variance. And to get the StdDev,
> I should actually use the intervals command?
>
>
>
> So, in the above case the Intercept for ~1|year gives the variance between
> years, the intercept for ~1|month %in% year gives the variance between
> months in a given year and the residual is the within month variance in a
> given year. Am I interpreting it correctly? I would divide each value by
> all the total sum to get the percentage variance explained? Also, why does
> the output say StdDev? Do I need to square it to actually get the variance
> for the groups?
>
>
>
> Also, the intervals command doesn’t seem to work with lme models. Anyone
> has any idea about that?
>
>
>
> Thanks
> Udita
>
>
>
> *From: *<mensurationist using gmail.com> on behalf of Andrew Robinson <
> A.Robinson using ms.unimelb.edu.au>
> *Date: *Monday, 13 August 2018 at 12:04 AM
> *To: *"Bansal, Udita" <udita.bansal17 using imperial.ac.uk>
> *Cc: *"r-sig-mixed-models using r-project.org" <r-sig-mixed-models using r-project.org
> >
> *Subject: *Re: [R-sig-ME] Interpretation of lme output with correlation
> structure specification
>
>
>
> 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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>
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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
>
--
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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