Dear lme4 list,
I am having trouble modelling regression with seemingly non-independent, 
nested observations and I think a mixed model approach (using *lme4* for 
example) is required. I am examining the effect of genetic relatedness 
of males on the proportion of offspring sired in cases of multiple 
paternity (multiple males contributing to a brood of eggs from one 
female). I have estimates of the genetic relatedness of each male and 
the female it mated with, as well as the proportion of offspring that 
male sired in the female's brood of eggs. There are 3-5 males 
contributing to a brood, and 9 separate broods sampled for paternity.

So the outcome is proportion sired, and the dependent variable is 
relatedness (Does relatedness have an effect on the proportion of 
offspring sired?). It seemed straightforward, however, the observations 
of male offspring proportion are not independent within a brood (the 
proportions sum to 1), and the female "treatment" is not given to each 
male, and should perhaps be treated as a random effect.

*My questions are: *1)Should observations be nested within a female, 
2)and/or should female be treated as a random effect (9 broods from 9 
females) or both? 3) should relatedness remain a fixed effect? Again, I 
am concerned that I have not adequately dealt with the non-independence 
of male proportion values.

I am looking to do this analysis in lme4 in R and have tried a variety 
of models, but feel as though I may be constructing them incorrectly...I 
also appreciate any advice on how test the significance of /relatedness/ 
if it is modeled as a random effect.

Here is an example of the data:

|IDFemale   Relat.   Prop.
1    A.12       .3
2    A.03       .02
3    B.02      .33
4   A         .23     .68
|

Thanks for any help or advice,
LP


	[[alternative HTML version deleted]]

