On second thoughts, won?t it be almost the same? If
~1|year/month expands to 1|year + 1|year:month (a random intercept for
each year plus for each month in each year)
~1|month/year expands to 1|month +1|month:year (here the random intercept
for month will be the same for January 2016 or 2017)?This would mean that
each month has an intercept and each year for each month (like the
highlighted part?).
At the end I would have
1. An intercept for each OR an intercept for each month
2. An intercept for each month in each year
Am I right?
Thanks
Udita
*From: *Guillaume Adeux <guillaumesimon.a2 at gmail.com>
*Date: *Monday, 13 August 2018 at 2:31 PM
*To: *"Bansal, Udita" <udita.bansal17 at imperial.ac.uk>
*Subject: *Re: [R-sig-ME] Interpretation of lme output with correlation
structure specification
Indeed your original model makes more sense.
~1|year/month expants to 1|year + 1|year:month (a random intercept for
each year plus for each month in each year)
whereas
~1|month/year expands to 1|month +1|month:year (here the random intercept
for month will be the same for January 2016 or 2017)
Depending on how a variable is coded, it can be crossed or nested but here
"month" has the same levels all the different years, so it has to be nested.
Cheers,
GA2
2018-08-13 11:41 GMT+02:00 Bansal, Udita <udita.bansal17 at imperial.ac.uk>:
Also, in continuation of my previous mail, I found that the error is
thrown for intervals() if the model is not correct.
My original model included: ~1|year/month (doesn?t give confidence
intervals)
The new model: ~1|month/year (gives me the confidence intervals)
Original model: looks at variation when going from one year to another
(~1|year intercept), and whether the effect of going from one month to
another changes for different years (~1|month %in% year intercept).
New model: looks at variation when going from one month to another
(~1|month), and whether the effect of going from one year to another
changes for different months (~1| year %in% month).
To me, the original model makes more sense. Am I not interpreting it
correctly? I used the Pinheiro and Bates book for this but maybe I am not
getting it right.
Anybody has any understanding on this?
Thanks
Udita
From: <mensurationist at gmail.com> on behalf of Andrew Robinson <
A.Robinson at ms.unimelb.edu.au>
Date: Monday, 13 August 2018 at 12:04 AM
To: "Bansal, Udita" <udita.bansal17 at imperial.ac.uk>
Cc: "r-sig-mixed-models at r-project.org" <r-sig-mixed-models at 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 at imperial.ac.uk
<mailto:udita.bansal17 at 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 at unimelb.edu.au<mailto:apro at unimelb.edu.au>>
Date: Saturday, 11 August 2018 at 11:16 PM
To: "r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models@
r-project.org>" <r-sig-mixed-models at r-project.org<mailto:
r-sig-mixed-models at r-project.org>>, "Bansal, Udita" <
udita.bansal17 at imperial.ac.uk<mailto:udita.bansal17 at 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 at unimelb.edu.au<mailto:apro at unimelb.edu.au>
Website: http://cebra.unimelb.edu.au/
On 12 Aug 2018, 7:34 AM +1000, Bansal, Udita <
udita.bansal17 at imperial.ac.uk<mailto:udita.bansal17 at 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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