Hello, 

I have difficulties to deal with multilevel model. My dataset is composed 
of 10910 observations, 1237 plants nested within 17 stations. The data set is not 
balanced. Response variable is binary and repeated.

I tried to fit this model

model<- glmmPQL( y ~ z1.lon*lun + z2.lat*lun + z1.lon*lar + z2.lat*lar + z1.lon*sca + z2.lat*sca +z1.lon*eta + z2.lat*eta,
 random = ~ lun + lar + sca + eta | sta/piante, family=binomial, data=variabili)

where  y is presence (1) or absence (0) of a flowering

lun, lar, sca, eta are level 1 variables

z1.lon, z2.lat are level 2 variables.

but during third iteration it stop because there is a singular matrix in solve.

I stopped it after two iterations, however the results are not correct.

How can I fit this data? Are there other functions that I can use?

I would be thankfull for all the insights.

Fabrizio Consentino

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