mixed-effects model with crossed random effects

Magdalena Wiedermann <mwiederm at ...> writes:

Dear list,

I am using the nlme package to analyze a mixed effects model. I am
dealing with crossed random effects, meaning that I have repeated
measures in time and space (on a split plot design). We are sampling
water at two depth within one of each vegetation treatment (3 veg
treatments) organized in 4 blocks.

I figured that the code including the spacial component only would be!?:
<-lme(responses~veg*depth*year*Month, random=~1|block/veg)

I am aware that 4 blocks is not pretty for it to be a random effect, but
I am not interested in it as fixed effects. Also I am aware that there
are very many philosophies and views on what a random effect is/should be.

Can anybody please help me with adding the temporal component in?
Samples on these plots were taken 5 times each year for 3 years => 15
times of repeated measures

I'd be more than happy about any suggestions, similar examples etc.
Thank you so much!
Lena

    It is possible to fit crossed random effects in lme (it's discussed
in one of the later chapters of Pinheiro and Bates), but it's a
bit of a hassle.  If you're willing to use lme4 instead (you can
use the lmerTest or pbkrtest if you need p-values, see ?pvalues)
this will be a little bit easier.

     Something like

   lmer(responses~veg + (veg|year/Month) + (veg|block), data= ...)

would seem to be a reasonable guess, although it may be too much
for your data since you will be estimating 3 3x3 variance-covariance
matrices of veg responses (within year, within Month-within-year,
within block).  I don't know whether you have trends over
the course of your time series (e.g. add a numeric covariate of
time period to the fixed effects) or consistent seasonal effects
(e.g. make your model (veg|year) + (veg|Month) + (veg|year:Month)) ...

    Ben Bolker