Some timings for lmer2 versus lmer
I enclose an R source file to do some comparative timings on lmer2 fits versus lmer fits and the output generated on the machine R-forge.R-project.org (Opteron 280 dual-core processors, R internal BLAS, 13 GB of memory). You can try running the script on your computer to get an idea of the timings. On some machines the lmer fit to the "star" data set will converge in considerably fewer iterations than on this machine. There is one point in the optimization where very small differences in the floating point operation orders cause a much better step to be taken. If you look at the verbose output you will see that the parameterization for lmer2 uses the relative standard deviation (as described in the Implementation vignette from the lme4 package) whereas lmer used the relative variance. Generally the relative standard deviation is more stable for the optimization. The other big difference in the optimization, shown in the last example, is that lmer evaluates the relative precision matrix (the inverse of the relative variance matrix) and therefore cannot allow variance components to go to zero. The value of a variance component is bounded below at 5e-10, which is why that particular number shows up in the verbose iterations. As described in the vignette, the relative variance matrix is used in lmer2 hence the lower bound on the variance component is at zero. -------------- next part -------------- A non-text attachment was scrubbed... Name: lmer2_test.R Type: application/octet-stream Size: 2967 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20070127/1ad3cb3d/attachment.obj> -------------- next part -------------- A non-text attachment was scrubbed... Name: lmer2_test.Rout Type: application/octet-stream Size: 45018 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20070127/1ad3cb3d/attachment-0001.obj>