Hi all,
I am trying to build a model that includes two random effects while also
using a correlation structure to account for spatial autocorrelation. It?s
a full factorial study on simulations of wildlife where individuals are
spread across landscapes, so one of the random effects (N) is crossed.
If I use nlme I can do this by reusing creating a new group factor by
pasting the three crossed factors together (would be land:barr:mort in
lme4), which I call ?lr'. The parameter estimates are similar, so it seems
ok. (Link to the data frame:
https://drive.google.com/open?id=0B096pYMrPnKAdC1FdWhCR3Z4bjg <
https://drive.google.com/open?id=0B096pYMrPnKAdC1FdWhCR3Z4bjg> )
ibr4 <- read.csv(?~/ibr4.csv?)
m1 <- lme(A ~ barr + mort, random = list(~cost | land, ~N | lr),
data=ibr4, method = ?ML?)
Once I try to do that along with a correlation structure, it complains
that there are incompatible formulas for ?random? and ?correlation?.
m2 <- lme(A ~ barr + mort, random = list(~cost | land, ~N | lr),
data=ibr4, method = ?ML?,
correlation = corExp(form = ~ x+y | lr))
I think it?s because it doesn?t know how to relate lr to land, because it
complains the same way when the only random effect is '~cost | land?.
However, when one random effect is in the model with a correlation
structure nested as ~x+y | land/barr/mort, it does work. But it doesn?t
seem to ever accept multiple random effects together with a correlation
structure. I know Pinheiro and Bates say in their book (p.163) that you can
build a crossed random-effects structure with pdBlocked and pdIdent, but
(1) it?s not clear to me how to do this for a single random effect, and (2)
it?s not clear to me that you could include multiple random effects in such
a structure. Am I misunderstanding how correlation structures and/or random
effects work? Let me know if you need more information about my data.
Thanks,
Karl
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