Dear Sara,
? - binary response variable: ?Dead? = bud survival, either dead (1)
? or alive (0)
? - Fixed factor: ?fTreatment?, numerical factor with 9 different levels
? - Fixed factor: ?fBud_type?, categorical factor with 3 levels
? - Fixed factor ?Species? (categorical) nested within ?fBud_type?,
? with 9 levels
? - Random factor ?fRep?, numerical, nested within ?Species?, with 24
? levels (i.e. coded sequentially to avoid confusion).
?
? The model I want to run is:
?
? M_bud_type1=glmer(Dead~fTreatment* fBud_type * fBud_type|Species +
? (1|fRep), family=binomial, data=species)
Are you sure you want this model and not
M_bud_type1=glmer(Dead~fTreatment + fBud_type + fBud_type|Species +
(1|fRep), family=binomial, data=species)
I guess you're problem comes from the a*b which stands for a+b+a:b,
then coupling with more than two variables and interaction/nesting
terms makes a not very clear formula.
Try specifying only main effects and suited interactions, using either
of the three syntaxes, and it my work better.
Hope this helps,
Best regards
PS: this issue exists in all kinds of models, including
lm/glm/lmer/lme...
--
Emmanuel CURIS
emmanuel.curis at parisdescartes.fr
Page WWW: http://emmanuel.curis.online.fr/index.html