"Fabrizio Consentino" <[EMAIL PROTECTED]> 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 [EMAIL PROTECTED] Statistics Department 608/262-2598 University of Wisconsin - Madison http://www.stat.wisc.edu/~bates/ ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html