models with fixed effets nested in random effects

I am having trouble defining a model that accounts for the data
structure but also allows the treatment effect to be estimated as a
fixed effect. I think I have it figured out but I would like to see what
some other opinions are regarding placement of a fixed effect in both
the random and fixed sections of a model formula.
 
There is 20 years of records at a variety of locations and subsites
within each location, with treatments for 1/2 of the years of records at
each location and subsite. The general structure to the data is:
    Location/Subsite/year
The treatment was applied:
    Location/Subsite/TREATMENT/year
 
The model format is:
    u5 <- lmer(ung ~ FEsection + MIT_UNMIT +
(1|FenceEnd/FEsection/MIT_UNMIT), family=quasipoisson(link =

        "log"))

 

Model output is:

    > summary(u5)

    Generalized linear mixed model fit using Laplace 

    Formula: ung ~ FEsection + MIT_UNMIT + (1 |
FenceEnd/FEsection/MIT_UNMIT) 

     Family: quasipoisson(log link)

      AIC  BIC logLik deviance

     1661 1733 -816.7     1633

    Random effects:

     Groups                         Name        Variance Std.Dev.

     MIT_UNMIT:(FEsection:FenceEnd) (Intercept) 0.268713 0.51838 

     FEsection:FenceEnd             (Intercept) 0.066643 0.25815 

     FenceEnd                       (Intercept) 0.213373 0.46192 

     Residual                                   1.371364 1.17105 

    number of obs: 1230, groups: MIT_UNMIT:(FEsection:FenceEnd), 93;
FEsection:FenceEnd, 58; FenceEnd, 7

     

    Fixed effects:

                    Estimate Std. Error t value

    (Intercept)    -1.808774   0.340329  -5.315

    FEsection2     -0.148425   0.349608  -0.425

    FEsection3     -0.066525   0.344542  -0.193

    FEsection4     -0.021216   0.346025  -0.061

    FEsection5      0.470120   0.333193   1.411

    FEsection6      0.459920   0.329556   1.396

    FEsection7      0.455736   0.381993   1.193

    FEsection8      0.006034   0.399353   0.015

    FEsection9      0.423509   0.383930   1.103

    FEsection10     0.062848   0.393176   0.160

    MIT_UNMITunmit  1.572822   0.188382   8.349

 

This seems fine to me, there are the propoer number of groups in the
data structure. The problem I have yet to wrap my head around is the
fact that the fixed effects are still in the random effects category as
well. Is that a problem? Should I be adding all terms with fixed effect
variable, including interaction terms, into the 'fixed effects' part of
the model equation? Since I am new to this LMER routine it is a bit
confusing to write.
 
Wayne Hallstrom
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