[R-sig-ME] Fw: lme4
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)  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
> 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
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
> 4 way interaction
Comparing previous results just for these terms --
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 *
cgroup:cgrammaticality 1.57010 0.36695 4.279 1.88e-05 ***
cgroup:cgrammaticality: 3.15344 1.42351 2.215 0.0267 *
As I said before, the new and old results
look the same to me for all practical
> 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>
More information about the R-sig-mixed-models