lme4
Ebi Safaie <safaie124 at ...> writes:
Dear Ben Bolker, Thank you very much for your informative reply. Yes, I followed Barr et al (2013). I did what you kindly sent me. I'm not sure I've done it correctly but it came to false It would be a good idea to check for a singular fit, i.e. t <- getME(mod.15,"theta") lwr <- getME(mod.15,"lower") any(t[lwr==0]< 1e-6) t <- getME(mod.15,"theta") > lwr <- getME(mod.15,"lower") any(t[lwr==0]< 1e-6) [1] FALSE
that's good -- that means that your model is at least bounded away from zero for constrained parameters.
I increased the number of iterations as you suggested summary(mod.15<-glmer(ErrorRate~1 cgroup*cgrammaticality*cHeadNoun*cVerbType+(1|itemF)+ (1+grammaticality*HeadNoun*VerbType|participantF),data=e3, family="binomial",na.action=na.exclude, control=glmerControl(optCtrl=list(maxfun=1e6)))) but it came to the following message Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.113924 (tol = 0.001, component 29) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge: degenerate Hessian with 1 negative eigenvalues
These warnings do suggest that your model is at the very least unstably fitted. You could try some of the strategies listed at http://rpubs.com/bbolker/lme4trouble1 to reassure yourself that the model fit is in fact OK. I want to emphasize again that your model is **not** actually fitting worse than it did before/with previous versions; rather, the default warning level has been turned up so that you're getting more warnings than before.
Actually the following two interactions are important for me because they are representing two hypothesis 2 way cgroup*cgrammaticality 4 way interaction cgroup*cgrammaticality*cHeadNoun*cVerbType
Comparing previous results just for these terms --
previous
est stderr Z P
cgroup:cgrammaticality 1.5796 0.3586 4.404 1.06e-05 ***
cgroup:cgrammaticality: 3.1326 1.3994 2.239 0.0252 *
cHeadNoun:cVerbType
current
cgroup:cgrammaticality 1.57010 0.36695 4.279 1.88e-05 ***
cgroup:cgrammaticality: 3.15344 1.42351 2.215 0.0267 *
cHeadNoun:cVerbType
As I said before, the new and old results
look the same to me for all practical
purposes.
Earlier, I used odds ratio to calculate the effect sizes (Table below) and I was able to dissociate between these two interactions (i.e., two hypotheses) via their effect sizes. Due to wider range of the lower and upper limits of 95% CI I supported the 2 way interaction.
Don't know what you mean here. Are you trying to distinguish which one has a larger effect? Assuming all your predictors are categorical (so that you don't have to worry about standardizing units), the two-way interaction has a smaller _effect_ but also smaller uncertainty, so it is more statistically significant.
Am I on the right track? Given that I want to use the newer version of lme4 (as you recommended) I would really appreciate your help to let me know what to do with this really complex design. Table 9.Experiment 1a: Fixed-effects from mixed-effects logistic regression model fit to data from both NSs and the NNSs for S-V agreement Main analysis Fixed effects: Odds Ratio (OR) 95% CI For OR
Your table got somewhat mangled in transition to the mailing list, but appears to be a slightly modified version of the summary() output, with odds ratios and Wald confidence intervals on odds ratios (i.e. based on exp(est +/- 1.96*std. err) appended). The questions about warning messages from lme4 and what to do about them are on-topic for this list, but these questions about how to interpret the fixed effects are pretty generic (e.g. they would apply pretty much equivalently to a regular linear or generalized linear model), and would be more appropriate for a more generic stats questions venue such as CrossValidated <http://stats.stackexchange.com> sincerely Ben Bolker