[R-sig-ME] lme4 feature request: standardizing data and false convergence
Adam D. I. Kramer
adik at ilovebacon.org
Mon Jun 21 23:24:55 CEST 2010
You can also avoid convergence errors in glmer by making sure your
expected coefficients are in the realm of 1e0 or 1e1. Standardizing works
because it mean-centers your data, so coefficients will probably be around
the mean...but you can fix things just as easily by dividing a variable with
a tiny coefficient by 100 or 1000.
...but...there's also a bit of an issue of HOW to standardized
in a multi-level model. For example, do you standardize a variable relative
to its grand mean? Relative to the level-1 mean? Or the highest level? Or
relative to each mean in turn? Etc.
Given that SAS has done this by default, as you suggest, which mean
do they standardize relative to?
On Mon, 21 Jun 2010, Jeffrey Evans wrote:
> When using glmer in lme4 I frequently get false convergence errors. Many
> have posted to this list about this topic. The solution seems to be to
> standardize the X matrix first, which is effective at resolving the issue.
> Is there, (or could there be in future versions) a simple way to request
> that glmer optionally standardize the data internally and return
> back-transformed parameter estimates (on the original scale)?
> It seems that SAS does this by default in PROC GLIMMIX (version 9.2) and
> rarely had issues with regression conversion. It would be a huge help.
> Jeff Evans
> Dartmouth College
> [[alternative HTML version deleted]]
> R-sig-mixed-models at r-project.org mailing list
More information about the R-sig-mixed-models