Hi

I am fitting random intercept and slope models on some GPS tracking data.  I have data from 113 tracks from 31 individuals with about 400 behavioural observations per track.

I am interested in looking at how individuals change their behaviour in response to an environmental variable (a) but want to control for the non-independence of points from individual tracks.

So I have these possible lmer models :


a)       lmer (behaviour ~ (1|bird/track) + (-1+a|bird))

b)      lmer (behaviour ~ (1|bird/track) + (0+a|bird))

c)       lmer (behaviour ~ (1|bird/track) + (a|bird))


I have two questions:

1)     Can you fit an intercept and slope with different random effect structures?  In theory I think this is ok but I have not see it done.

2)     I am interested in the differences in covariance structures in the three models.  I thought that model (a) set the covariance between the slope and intercept for bird to 0 but from looking at previous posts in the forum, it seems that model (b) may do this?  In which case does model (a) set the covariance to -1?
My understanding is that model (c) allows a symmetric variance-covariance matrix (which is what I want) but I am concerned that this model is fitting two intercepts for bird?

Ideally I want the intercept for bird to be allowed to covary with the slope for bird (I don't want to constrain it  to 0 or 1) but I am unsure if my problem is in the syntax or whether I don't fully understand how the model partitions the variance in nested random effects and that maybe the model I want to fit is not possible.

Any advice on creating the models would be much appreciated!

Many Thanks

Sam

Dr Samantha Patrick
EU INTERREG Post Doc
Davy 618
Marine Biology & Ecology Research Centre
University of Plymouth
Plymouth
PL4 8AA

T: 01752 586165
M: 07740472719


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