


Hi al, 


I am trying
to fit a general lineal mixed modal to a small unbalanced dataset of countdata
(poisson errors). My model would be: 


glmer(indep~dep1+dep2+dep3+dep4+dep5+(1|random1)+(1|random2),family=

poisson,data)

 

Error en
asMethod(object) : matrix is not symmetric [1,2]

Ademįs: Mensajes de aviso perdidos

1: In mer_finalize(ans) :

  Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 432

2: In
mer_finalize(ans) :

  Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 432

3: In
mer_finalize(ans) : false convergence (8)

 

That
isn“t a surprising R-answer, as lmer doesn“t work with several crossed (not
nested) random factors if errors are not Gaussian. 
I read about a mysterious" hacked version of glmer to allow per-observation random effects" in Zuur et al, 2009? 

Any one
knows if there is (already) a function that does the job? 
Are
there any best alternatives?
thanks
david



 		 	   		  
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