Thanks for your feedback.
Actually Plant is nested since High and Low are qualitative relative
values (High in Michigan is not the same as High in Sienna).
S Ellison wrote:
The Plant classification is not nested; it's an effect across all
countires and states and probably a fixed effct (assuming you want to
measure its size or significance). But the state is nested in country.
That would suggest to me
lmer(Reaction~Drug+Plant+(1|Country/State),...)
(or Plant*Drug, if you want an interaction)
However, though your states are nested in countrythey are readily
identifiable by lmer as different for each country (you do not have
country1: State1, State2..., Country2:State1,State2..) so the nesting by
country probably doesn't need to be specified. I think the above would,
_in this case_ be almost identical to
lmer(Reaction~Drug+Plant+(1|Country)+(1|State),...)
Ubuntu Diego <ubuntu.di...@gmail.com> 01/12/2009 16:22:09 >>>
I saw different specifications for Random Effects and I'm confused
about
the use of "/" and the use of "(0+...|)" .
Let say we have a nested structure where some countries have some
several plants in different states and we measure the reaction to a
drug.
The list of Countries = USA, France, Italy
The States for USA = Michigan, Florida, California
The States for France = Paris, Orleans
The States for Italy = Venezia, Sienna, Florence, Rome, Napoli ,
Sicilia
Plants were classified as High and Low
is this the way to specify a possible model ?
lmer(Reaction ~ Drug + (1| Country / State / Plant) , data)
or should I use something like this
A) lmer(Reaction ~ Drug + (0| Country / State / Plant) , data)
B) lmer(Reaction ~ Drug + (1| Country ) + (0+Country | State / Plant) ,
data)
C) lmer(Reaction ~ Drug + (1| Country ) + (0+Country | State / Plant) +
(0+Country + State | Plant), data)
D) lmer(Reaction ~ Drug + (1| Country ) + (0+Country | State ) +
(0+Country + State | Plant), data)
Thanks
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