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

Douglas Bates bates at stat.wisc.edu
Tue Mar 10 17:40:54 CET 2015

It is not clear that the error message you quote is a result of too many

As Thierry stated, it would help to have a small reproducible example.

On Tue, Mar 10, 2015 at 9:24 AM Asaf Weinstein <asafw.at.wharton at 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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