[R] Mixed effect models

[Eleanor Spratt]

I am using two mixed effect models. Firstly, what I am trying to do is to
compare green roofs abundance with brownfield, green roof with green space
abundance, and finally green
space with brownfield abundance. I am unsure if I have done the
correct model. I have to use a mixed effect model because my data is
nested.

This is the code and output

> model1<-lmer(Total.abundance~Habitat+(1|Site)+(1|Week),REML=FALSE,family=poisson)
> summary(model1)

Generalized linear mixed model fit by the Laplace approximation
Formula: Total.abundance ~ Habitat + (1 | Site) + (1 | Week)
 AIC  BIC logLik deviance
 1780 1795 -884.9     1770
Random effects:
 Groups Name        Variance Std.Dev.
 Site   (Intercept) 0.62318  0.78941
 Week   (Intercept) 0.13883  0.37260
Number of obs: 150, groups: Site, 15; Week, 10

Fixed effects:
                  Estimate Std. Error z value Pr(>|z|)
(Intercept)          2.8116     0.3740   7.517 5.59e-14 ***
HabitatGreen roof   -0.8676     0.5040  -1.721   0.0852 .
HabitatGreen space   0.2008     0.5021   0.400   0.6892
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr) HbttGr
HabittGrnrf -0.668
HabttGrnspc -0.671  0.498

>From this I understand that green roof has a negative trend with
brownfield, and green space has no significance with brownfield. But
what about green roof and green space???? Is there a way of
interpreting this information from the above data. Is it like ANOVA
where you have to manually calculate the p value. Or do I have to
simplify this model by reducing my Habitat factors levels (e.g.
combining green space and brownfield together).

My second mixed effect model is seeing if environmental factors influence
the mixed effect model, but I want to use interactions. When I plot this I
get an error message.

> model1<-lmer(Total.abundance~(area+Hemeroby+Age+isolation+Height+Bare.ground+Grass+Non.grass)^2+(1|Site)+(1|Week),REML=FALSE,family=poisson)

Error: inner loop 1; cannot correct step size
In addition: Warning message:
step size truncated due to divergence 

Thus I tried it without interactions- 
> model1<-lmer(Total.abundance~area+Hemeroby+Age+isolation+Height+Bare.ground+Grass+Non.grass+(1|Site)+(1|Week),REML=FALSE,family=poisson)

but with a couple of simplifications of the model the intercept was not
significant, so I dont' know what to do.

Kind Regards

Ellie

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