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Multilevel in R
2 messages · Fabrizio Consentino, Douglas Bates
"Fabrizio Consentino" <consentino at infinito.it> writes:
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.
Start with a simpler model. Try random = ~ 1 | sta/piante and see if that converges. You could also try function GLMM from the lme4 package.
Douglas Bates bates at stat.wisc.edu Statistics Department 608/262-2598 University of Wisconsin - Madison http://www.stat.wisc.edu/~bates/