[R-sig-ME] lmer model specification

[jude girard]

Hi,

I am hoping the list can help me ensure that I am specifying my model
correctly.

In my study, we compared invertebrate biomass in paired organic and
conventional fields.  I had 9 conventional and 9 organic fields, for a
total of 18. Each field was sampled in May and in July.  At each
sampling time, two transects, each of 4 traps, were placed in each
field.

My fixed effects are management type and month (I expect invertebrate
abundance to be higher in July than in May), and the interaction
between them.  At first I specified my random effects as
pair/month/transect.  However, when I run this model

model.edge<-lmer(biomass~management*month+(1|pair/month/transect), data=edge)
summary(model.edge)

Linear mixed model fit by REML
Formula: biomass ~ management + month + (1 | pair/month/transect)
   Data: edge
   AIC   BIC logLik deviance REMLdev
 535.8 559.8 -260.9    513.4   521.8
Random effects:
 Groups                Name        Variance Std.Dev.
 transect:(month:pair) (Intercept) 0.071400 0.26721
 month:pair            (Intercept) 0.000000 0.00000
 pair                  (Intercept) 0.018794 0.13709
 Residual                          0.491406 0.70100
Number of obs: 230, groups: transect:(month:pair), 36; month:pair, 18; pair, 9

Fixed effects:
              Estimate Std. Error t value
(Intercept)   -1.84429    0.11147 -16.545
managementorg  0.29042    0.09474   3.065
monthR2        0.45889    0.12924   3.551

Correlation of Fixed Effects:
            (Intr) mngmnt
managemntrg -0.399
monthR2     -0.571 -0.022


the variance for month:pair is 0, which I think might be because the
month is already specified in the fixed effects?  So, now I am
wondering if a better way to run the model might be
model2.edge<-lmer(biomass~management*month+(1|pair/transect.unique),
data=edge)  where is transect is treated as a unique level,
irrespective of which month it was run in?

When I run the second model I get

summary(model2.edge)
Linear mixed model fit by REML
Formula: biomass ~ management * month + (1 | pair/transect.unique)
   Data: edge
   AIC   BIC logLik deviance REMLdev
 524.2 548.3 -255.1    501.7   510.2
Random effects:
 Groups               Name        Variance Std.Dev.
 transect.unique:pair (Intercept) 0.17567  0.41913
 pair                 (Intercept) 0.01887  0.13737
 Residual                         0.39778  0.63070
Number of obs: 230, groups: transect.unique:pair, 70; pair, 9

Fixed effects:
                      Estimate Std. Error t value
(Intercept)            -1.8028     0.1374 -13.122
managementorg           0.1665     0.1866   0.892
monthR2                 0.3745     0.1842   2.033
managementorg:monthR2   0.1866     0.2645   0.705

Correlation of Fixed Effects:
            (Intr) mngmnt mnthR2
managemntrg -0.655
monthR2     -0.663  0.488
mngmntrg:R2  0.461 -0.704 -0.697

I'd appreciate if anyone could give me some insight on this

Thanks, Jude Girard