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 Website: http://cebra.unimelb.edu.au/
On 12 Aug 2018, 7:34 AM +1000, Bansal, Udita <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 [[alternative HTML version deleted]]
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