[R-sig-ME] convergence: nearly unidentifiable: very large eigenvalue-Rescale variables?
@ouhey|@@ghebghoub @end|ng |rom gm@||@com
Thu Apr 18 15:22:55 CEST 2019
Yes. Duration is a predictor for 28 words. So there are 28 values for
duration, in a range of 0 to 400, e.g. c(0, 10, 0, 320, 8, 35, 2, 400, 10,
Yes, I applied log() since its a duration in seconds, but values of 0
turned to infin values does the model did not recognise.
I used scale() , though the model converged but it resulted in certain
negative values for duration, I found that the scaling made the values
quite close, no much difference between them and I am worried to what
extent this affects my hypothesis testing that the duration should have an
effect on DV ? I found no significant effect could this be due to scale()
Thank you very much,
On Thu, 18 Apr 2019 at 12:40, Phillip Alday <phillip.alday using mpi.nl> wrote:
> On 18/4/19 1:14 pm, Souheyla GHEBGHOUB wrote:
> > Hi everyone,
> > I have a continuous predictor, duration, of 28 levels but with a large
> > variance (from 0 to 400).
> Um, how do you have a continuous predictor with levels? Do you mean that
> there are only 28 different/unique observed values within that
> continuous predictor?
> Did you try applying scale() to duration? Or if duration is a response
> time, maybe log()?
> How many subjects do you have? How many words? Are they fully crossed?
> This inhibits convergence. I used all techniques
> > mentioned in this link lme4 convergence warnings: troubleshooting
> > <
> > but
> > nothing worked : ( ?
> > Here is my model:
> > *mod <- glmer(response~duration + wfpre + sequence + verbalfreq + PoS +
> > Characters + (1|Subject) + (1|Word), glmerControl(optimizer="bobyqa",
> > optCtrl = list(maxfun = 2e5)), **data = df, family = 'binomial')*
> > Could you please assist me with this?
> > Thank you
> > Souheyla
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