Dear All, I am an unsophisticated R user just beginning to use generalized linear mixed-effects modelling. I wonder if anyone can help me with a couple of questions:
1) I'm using the following code: > early.glmm = lmer(bup ~ train + fam + allo + sengrp + subgrp + train:fam + (1|sub) + (1|sen), data=dat.early, family="binomial") > print(early.glmm, corr=FALSE) This gives me estimates, z-scores and p values for the fixed effects, e.g.: #Fixed effects: # Estimate Std. Error z value Pr(>|z|) #(Intercept) -1.55152 0.60721 -2.555 0.01061 #train0 -0.94079 0.29593 -3.179 0.00148 #train6 -0.21783 0.27753 -0.785 0.43252 #train12 -0.67147 0.28260 -2.376 0.01750 #famU -1.47283 0.52289 -2.817 0.00485 #alloMis 0.26959 0.13095 2.059 0.03952 #sengrpd -1.03809 0.65300 -1.590 0.11190 #sengrps -1.83014 0.69729 -2.625 0.00867 #sengrpt 0.37461 0.63814 0.587 0.55718 #subgrpb 0.79949 0.51828 1.543 0.12293 #subgrpc -0.59432 0.28730 -2.069 0.03858 #subgrpd 0.76644 0.51872 1.478 0.13953 #train0:famU 0.82564 0.38161 2.164 0.03050 #train6:famU 0.24475 0.36384 0.673 0.50115 #train12:famU 0.03477 0.38802 0.090 0.92860 What it doesn't provide is an overall chi-squared and p for main effects and the interaction. Is it possible to obtain these? 2) If I experiment with a more complex random effects structure than in the above (e.g. (1+fam|sub)), I get complaints about convergence. Likewise, if I try to include covariates as well as factors. Does anyone know why this might be? Very many thanks for any tips! Best wishes Rachel -- Dr Rachel Smith RCUK Academic Fellow Department of English Language University of Glasgow 12 University Gardens, Glasgow G12 8QQ [EMAIL PROTECTED] +44 (0)141 330 5533 _______________________________________________ R-lang mailing list [email protected] https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang
