glmer does not converge, how inaccurate is using nAGQ = 0?
Paolo sent me the output, and there are a couple of problems: 1. The intercept is very large about 94, which may cause some problems. This can be fixed by centering (subtracting the mean or some other suitable value) the continuous variables. 2. The standard deveriation for the random effect is large. This usually requires more quadrature points. 3. As you have a huge number of observations and groups it doesn't really matter that much about the approximations. Almost anything will be significant. I would just try centering the continuous variables. 4. You can also try another optimiser, as in a post from Ben, which will probably remove the convergence error.
On 2 April 2015 at 21:10, Ken Beath <ken.beath at mq.edu.au> wrote:
I think you have other problems, although sometimes this can be a break down in the approximations, and increasing nAGQ can work. Either your model is not identifiable, which means that it is overparameterised or some of your coefficients have become excessively negative, or you may have a very high random effect variance. The last will be helped by increasing the quadrature points, the second may be. Without seeing the output it is hard to tell. On 2 April 2015 at 20:36, Paolo Fraccaro <paolo.f.genova at gmail.com> wrote:
Hi, thanks for your suggestions. I left it going overnight still with nAGQ=1 and this time I got this warnings: Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00191069 (tol = 0.001, component 4) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue - Rescale variables? Is the solution of increasing nAGQ still the best thing to do? Many thanks, Paolo On 1 April 2015 at 23:06, Ken Beath <ken.beath at mq.edu.au> wrote:
You could use a value of nAGQ that is higher, start with 5 and work up. How good the approximation is, depends. If you are having convergence problems it probably isn't. On 2 April 2015 at 01:23, Paolo Fraccaro <paolo.f.genova at gmail.com> wrote:
Hi I have a dataset of ~200k piece of hardware tested yearly for 10 years or until failure (~15k). Therefore, the overall dataset size is ~2,000k. I'm trying to fit a mixed effects logistic model with glmer, but the model does not converge with the default settings. I tried to increase the number of max iterations allowed (from 20 to 100) but still it does not converge. I then set the nAGQ = 0 and obtained the less accurate estimate of the model. My questions would be: Do you have any idea of what parameters I could modify to try to make the model converge? How inaccurate is using nAGQ = 0? Many thanks. Paolo
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