[R-sig-ME] Fw: lme4
Ben Bolker
bbolker at gmail.com
Sat Oct 18 18:04:24 CEST 2014
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
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