[R-sig-ME] [R] lme4 : lmer : convergence problem and other errors

[yufeng at nsm.umass.edu]

Thank for your suggestions but why u want to center x variables around zero in
that way? I just don't understand what is the advantage by doing that.

Yufeng

Quoting Gillian Raab <gillian.raab at googlemail.com>:

> 2008/7/8 <yufeng at nsm.umass.edu>:
>
> > Please see below:
> >
> > > 1) What version of lmer are you running? The new version post 23/6/08
> > copes
> > > with difficult likelihoods better.
> >
> > I used the most up-to-date version.
> >
> > > 2) Have you changed anything in the elements of the control parameter.
> > The
> > > pre 23/6 verion had several parameters and the later one fewer. In
> > > particular you can increase the iterations
> >
> > How could I increase the iterations?
>
> READ THE HELP FILE UNDER LMER AND THE CONTROL  PARAMETER
>
> >
> >
> > > 3) Have you centred your x variables so they have means around zero. If
> > not
> > > you should always do this as it will make the fitting easier especially
> > with
> > > quadratic terms. This ought to have been my first suggestion.
> >
> > Do u mean the random effects of x's should be centered around 0? I didn't
> > do
> > that and I don't know how to do that in R? Could U tell me how? Thanks!
>
> NOTHING FANCY JUST CALCULATE NEW X VARIABLES BY SUBTRACTING THE MEAN VALUES
>
> >
> >
> >
> >
> > > 4) Having another look at your model you say it is non-linear, but it
> > looks
> > > linear to me if you set the squared terms as covariates too.
> >
> > You are right the model should be linear.
> >
> > >
> > > Good luck
> > >
> > > Gillian Raab
> > > Edinburgh
> > >
> > > On 03/07/2008, yufeng at nsm.umass.edu <yufeng at nsm.umass.edu> wrote:
> > > >
> > > > Dear R-user,
> > > >
> > > > I am trying to use the R "lmer" function in lme4 package to fit a non
> > > > linear
> > > > mixed effects model. The model I wand to fit is at an individual level
> > with
> > > > 4
> > > > parameters. For all parameters both fixed and random effects have to be
> > > > estimated, as well as their covariance matrix (see the formula bellow).
> > > > y~x1+x1^2+x2+x2^2.
> > > >
> > > >
> > > > I tried to fit the model with my data sets, but most of the time, R
> > returns
> > > > an
> > > > error message.
> > > > there are three main types of errors :
> > > >
> > > > - In mer_finalize(ans, verbose) :
> > > >   function evaluation limit reached without convergence (9)
> > > >
> > > > -  there are false convergence (8)
> > > >
> > > > -there are singular convergence (7)
> > > >
> > > > Do you know how to resolve these problems. Is there a way to modify the
> > > > parameters of the maximization algorithm to avoid these error messages?
> > > >
> > > > Thank you for your help and answers.
> > > >
> > > > Regards,
> > > >
> > > > Yufeng Zhang
> > > >
> > > > _______________________________________________
> > > > R-sig-mixed-models at r-project.org mailing list
> > > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > > >
> > >
> > >
> > >
> > > --
> > > Gillian M Raab
> > > 10 Ainslie Place EH3 6AS
> > > tel 0131 226 6234
> > > mobile 07748 678 551
> > >
> >
> >
> >
> >
>
>
> --
> Gillian M Raab
> 10 Ainslie Place EH3 6AS
> tel 0131 226 6234
> mobile 07748 678 551
>