false convergence (8) with binary covariate (0, 1) but NOT if covariate is (no, yes)?
a) Why is that so?
Is it possible that you coded the variable as an integer before and as a factor now? I'm not sure if lmer treats a two-level integer as a factor, but that might have been it.
b) How can I fix the false convergence problem if my covariate has also a binary format?
You might try to apply sum contrasts, i.e., -1/2 and 1/2. Everybody please correct me if this is nonsense. Best, Paul
On 8 Mar 2011, at 14:25, Julia Sommerfeld wrote:
Dear list,
The false convergence warning message (8) seems to be a common problem in
lmer. I've searched the archives to find a solution and found out that one
approach would be standardizing my covariate to mean-center the data.
However, what if the covariate itself (in this case SameMate) has a binary
value (0,1)?
After testing for the effect of the breeding success on site-fidelity (and
finally understanding the summary output), I would like to account for the
effect of breeding success (successfull=1, unsuccessfull=0) on mate-fidelity
(mate-fidelity=1, no mate-fidelity=0).
I.e. does the model including BreedSuc1 fits better than without it:
fm<-lmer(SameMate~BreedSuc1+(1|Bird),family="binomial")
fm1<-lmer(SameMate~1+(1|Bird),family="binomial")
Both models get the warning message:
In mer_finalize(ans) : false convergence (8)
anova(fm,fm1)
Data:
Models:
fm1: SameMate ~ 1 + (1 | Bird)
fm: SameMate ~ BreedSuc1 + (1 | Bird)
Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
fm1 2 48.659 52.946 -22.330
fm 3 50.342 56.771 -22.171 0.3174 1 0.5732
Now the weird thing:
I simply changed the coding of my covariate from "0,1" to "no, yes". Now,
only model fm1 gets the false convergence message:
fm<-lmer(SameMate~BreedSuc1+(1|Bird),family="binomial") fm1<-lmer(SameMate~1+(1|Bird),family="binomial")
Warning message:
In mer_finalize(ans) : false convergence (8)
Data:
Models:
fm1: SameMate ~ 1 + (1 | Bird)
fm: SameMate ~ BreedSuc1 + (1 | Bird)
Df AIC BIC logLik Chisq Chi Df
Pr(>Chisq)
fm1 2 47.490 51.468 -21.745
fm 3 54.096 60.063 -24.048 0 1 1
a) Why is that so?
b) How can I fix the false convergence problem if my covariate has also a
binary format?
I would highly appreciate suggestions and ideas.
Cheers,
Julia
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