Hi Emma,

Yes site==subject here, this is the unit that has been measured repeatedly.


I am not interested in the between sites differences in intercepts - overall
> site level effects in themselves, i.e. I don't care if there is a difference
> between sites in terms of intercept, as I would expect there to be as they
> are a random selection of sites, which all start with different actual
> values.
>

That's OK then, treat site as a random effect (1|site)

> I am interested in how the response changes between treatments within site

OK

> and whether this pattern (slope) is the same across sites.

If the levels of your treatment are factorial then you don't really have
enough data for this - this is the treatmentXsite interaction. If the levels
of your treatment are continuous, i.e you can plot response against
treatment as a line not a bar chart, then you probably can do this. But from
your description it sounds like the levels are factors.

It's quite possible that SPSS treated them as continuous and hence it "just
worked"


What does the output from this look like?

model1<-lmer~(minutes~treatment+(1|site), data = data)

This should tell you what you want to know - if you have treatment coded
correctly i.e. as a factor.


and this

xyplot(minutes~treatment|site, data = data)


Possibly you want to run the model like this


model1<-lmer~(minutes~treatment - 1 +(1|site), data = data)

with no intercept. Then you should get an estimate of the response for each
level of your treatment rather than estimates of the difference between
treatments 2,3,4 and level 1.


Andy.

	[[alternative HTML version deleted]]


