Crossed random effects in lmer?
On Wed, 23 Mar 2011, Julia Sommerfeld wrote:
1. If I want to test if BreedSuc1 influences SameMate and SameSite, shouldn't I account for the fact that the BreedSuc1 value of Bird E and F is exactly the same, since they are a pair? 2. Shall I use lmer with Pair as a random effect? What about the individual Bird? If I look at site-fidelity (and mate-fidelity?), I'm looking at the behaviour of the individual, rather than the behaviour of the pair. Could I use a lmer model including Pair and Bird? But how can I relate the random effect "Pair" to "BreedSuc1" and "Bird" to "SameMate", "SameSite"? Crossed random effects?
If you had enough data, then you would include a Pair effect as well as an individual effect - I would imagine there are incompatibilities among birds as in other species ie A+B, C+D less success than A+D, B+C. I'm guessing that you don't have enough data, though. Mechanistically, you are arguing that bird behaviour in season t+1 is a direct response to actual success in season t, so paths from both the latent variables (perceived mate fitness ?) and manifest variable (success) have to appear in the model. For the test of your specific hypotheses, all these random effects are nuisances. If it were me, I try to fit a simple marginal model as per your glm() models, and assess significance by simulation. A delete-d jackknife might be one way to give roughly correct standard errors. This is a method where you generate pseudosamples by deleting a random set of observations. Shao J, Tu D (1995): The jackknife and bootstrap. New York: Springer talk about this. SE=((n-d)/dm * Sum(r_i-mean(r_i, i=1, m))^2)^1/2 where d is the number of observations dropped for each pseudosample, ri is the test statistic value for the ith pseudosample, m is the number of pseudosamples, and n is the number of observations. I only mention this because it is not very difficult to do ;) and seems to work for complicated covariance structures.
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