[R-sig-ME] lmer fails when too many observations

[Thierry Onkelinx]

Dear Asaf,

Can you provide a reproducible example of the error? That should be easy
since you are simulating data.

It is not clear to me what kind of model you are fitting.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

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what the experiment died of. ~ Sir Ronald Aylmer Fisher
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ensure that a reasonable answer can be extracted from a given body of data.
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2015-03-10 2:52 GMT+01:00 Asaf Weinstein <asafw.at.wharton op gmail.com>:

> Dear lmer community,
>
> I am trying to run a simulation for a two-way random-effects model with
> unbalanced design (ie, unequal number of observations per cell) and no
> interaction.
> It's especially important for me to be able to run the lmer/blmer functions
> when the number of (column and row) random effects is large, say 100, and
> with possible replicates in each cell.
> The problem is that lmer() works with the full vector of observations, as
> opposed to working with the cell averages (which is a sufficient
> statistic), and the methods fails pretty quickly when there are replicates
> (because the response vector is too big, I suppose). I get the following
> error:
>
> *Error in get("checkConv", lme4Env)(attr(opt, "derivs"), opt$par, ctrl =
> control$checkConv,  : *
> *  (converted from warning) Model failed to converge with max|grad| =
> 0.00244385 (tol = 0.002)*
>
> Just to give an example: suppose there are R=100 row effects, C=100 column
> effects, and 5 replicates in each cell. The vector of individual
> observations is of length 100^5 (lmer fails), while the vector of cell
> averages is of length 100^2 (a size which causes no problem for lmer).
> My question is whether there is a way to tell lmer() to work with the
> sufficient statistic (of course, the conditional covariance is no longer
> c*Identity, a fact which is used in the implementation of lmer (according
> to documentation) ).
>
> Thank you very much and I hope I was clear!
>
> Asaf
>
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