I would be very grateful for some advice concerning specification of a mixed
model using lmer() in lme4.

 

I have measurements of growth rates of individual fishes (G) at 20 sites
(S,random) in three years (Y,random) and therefore at 60 site-year
combinations (SY,random). I would like to explore:

1)      The spatial and temporal structure of growth variation- does growth
rate vary between S, Y or SY.

2)      Whether growth variation is related to a set of continuous
environmental variables (E1, E2, E3, E4, all fixed).

I am concerned by the fact that, unlike most previous examples, my
environmental variables are measured at the level S (E1=wave fetch, E2=tidal
range) or SY (E3=competitor density, E4=prey density). Therefore, there is
one measurement of E1 and E2 for each of the 20 sites (S), regardless of the
year. Similarly, there is one measurement of E3 and E4 for each site and
year (SY). On the other hand, there are many measurements of the response
variable, G, within SY. In my case, therefore, fixed effects only vary among
(not within) random groups: in previous examples I have seen, fixed factors
vary within random groups.

 

My 'best guess' at the appropriate model specification is:

 

lmer(G~E1+E2+E3+E4+(1|Y)+(1|S)+(1|YS))

 

My approach has then been to fit the model with REML and sequentially drop
the random spatial / temporal terms. This has indicated that the random
structure can be reduced as follows:

 

lmer(G~E1+E2+E3+E4+(1|YS))

 

I then sequentially dropped fixed effects (E1-E4) from the ML fitted model
to assess the importance of each environmental variable.

 

I would greatly appreciate any insights as to whether my model specification
and approach are correct since I have not encountered this data structure in
previous examples of lme4 applications.

 

~Ben

 


	[[alternative HTML version deleted]]


