[R-lang] questions re: generalized linear mixed-effects modelling

[Rachel Smith]

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
R.Smith at englang.arts.gla.ac.uk
+44 (0)141 330 5533