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I was actually able to install the old lme4 version and did indeed produce my previous results. For those interested, below is a summary of the steps I followed. There is more detailed information at for those that want to build R as well as packages, but for those only interested in packages, perhaps the instructions below well be more useful. (thanks to Henrik Parn for several useful pointers). 1) The Toolset Install the R Toolset from I installed it directly under C:\ Although the instructions during installation mention that there are several remaining tasks to complete installation, it appears that these are only necessary if one wants to build R in addition to just add-on packages. 2) The old version of the add-on package I downloaded the old version of lme4 that I needed from I put it under C:\Documents and Settings\parn\Desktop\lme4_0.99875-9.tar.gz" and did NOT unpack it. 3) Setting the PATH: I added the following to the beginning of my PATH variable: C:\Rtools\bin;C:\Rtools\perl\bin;C:\Rtools\MinGW\bin;C:\R\R-2.7.1\bin; I didn't add the other parts mentioned at . My entire path was now thus: C:\Rtools\bin;C:\Rtools\perl\bin;C:\Rtools\MinGW\bin;C:\R\R-2.7.1\bin;C:\Program Files\MiKTeX 2.6\miktex\bin;%SystemRoot%\system32;%SystemRoot%;%SystemRoot%\System32\Wbem;C:\Program Files\WinLD; I noticed also that the change in the path was not dynamic, i.e., for it to take effect I had to exit the DOS prompt and re-enter the DOS prompt, then type "path" at the DOS prompt to see make sure that it had taken effect. 4) Deleting old version of lme4: I deleted the entire folder of the unwanted version of lme4, on my machine located at C:\R\R-2.7.1\library 5) R CMD At the DOS prompt, I typed: R CMD "C:\Documents and Settings\parn\Desktop\lme4_0.99385-9.tar.gz" A 'new' lme4-folder with all its contents was created 'automatically' in the appropriate place, i.e. here: C:\R\R-2.8.1\library. I did not specify this target myself. Thus, I could load the package from the menu interface after starting R. On 2/26/09 1:17 PM, "Douglas Bates" < bat ... > wrote: On Thu, Feb 26, 2009 at 10:31 AM, Afshartous, David < DAfs ... > wrote: All, For a paper revision I'm trying to reproduce some results from an old lmer fit with Rv2.7.1 prior to 5/28/08. However, when I currently load Rv2.7.1 and lmer, the variance component estimates are slightly different than the original fit; the sessionInfo() is as follows: sessionInfo() R version 2.7.1 (2008-06-23) i386-apple-darwin8.10.1 locale: en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] lme4_0.999375-24 Matrix_0.999375-11 lattice_0.17-8 loaded via a namespace (and not attached): [1] grid_2.7.1 nlme_3.1-89 Thus, I assume that I need to use the same older version of lme4 and/or Matrix which might be responsible for the difference in the results? If this is possible, how is this done? Cheers, David PS - for whatever it's worth, if I do the fit with lme (nlme_3.1-89) under Rv2.7.1 the results are closer to the original lmer results. ___________________________________________________ Original lmer fit from 5/08: Model 2: AIC BIC logLik MLdeviance REMLdeviance 2813 2843 -1397 2829 2795 Random effects: Groups Name Variance Std.Dev. Corr subject (Intercept) 2226.3 47.183 Drug 2132.9 46.184 -0.865 Residual 13673.6 116.934 Current lmer fit: AIC BIC logLik deviance REMLdev 2815 2849 -1397 2830 2795 Random effects: Groups Name Variance Std.Dev. Corr Patient_no (Intercept) 2165.1 46.531 Drug.full.reverseC 1386.3 37.233 -1.000 Residual 13947.5 118.100 Notice the large change in the estimated correlation with very little change in the log-likelihood or deviance. This is an indication that the model is over-specified. Are you able to install R packages from the sources? If so, you could try the branches/allcoef version from the SVN archive. On an optimization problem like this it may be more successful in converging to the global optimum instead of the local optimum. This, by the way, is why I am always looking for better optimization code to incorporate in R. The code in the nlme and lme4 packages just evaluates the log-likelihood or the REML criterion for the model at the observed data and a proposed value of the parameters. The actual optimization is done by the nlminb optimizer which is based on very old Fortran code written by David Gay. Even though the code is old this optimizer is, in my experience, more reliable than the optimizers used by optim and by nlm. It is surprisingly difficult to find good optimization code that is covered by an open source license. There is not a strong tradition of open source code in the numerical analysis world. Many users are enthused about the ipopt library (projects.coin-or.orgg/Ipopt) but even though that code is open source it depends on other software, some of which is commercial. The optimization in lme4 is minimization of a real-valued function of real parameters, some of which are subject to non-negativity constraints. It is not an unconstrained optimization problem but the constraints are very simple. The objective function can be evaluated and, in theory, the gradient can also be evaluated. However, for models with non-nested random effects evaluation of the gradient is much, much more difficult and time consuming than is evaluation of the objective function. Thus the ideal optimizer would allow for simple "box constraints" on the parameters and would be derivative-free or at least allow for numeric evaluation of the gradient. If anyone knows of such code covered by a valid open-source license I would be delighted to hear of it. Current lme fit: AIC BIC logLik 2814.611 2848.638 -1397.305 StdDev Corr (Intercept) 47.21031 (Intr) Drug.full.reverseC 46.14014 -0.866 Residual 116.93541 _______________________________________________ R-si ... @r-project.orgg mailing list _______________________________________________ R-si ... @r-project.orgg mailing list Your problem is related to the theory underlying linear models (and is an example as to why it is important to understand the theory, not just know how to plug numbers into a computer). The lm function is based on theory that assumes the y variable in normally distributed with the mean of that normal based on the model and the x values. This allows the predict function for lm to create prediction intervals based on the normal distribution, the predicted mean of that distribution, the estimated standard deviation, and the uncertainty in the predicted mean. Note that if your y variable is not normally distributed, but the sample size is large enough for the Central Limit Theorem to hold, then the confidence intervals will be approximately correct, but the prediction intervals will probably not be. When you switch to a robust regression approach, the assumption is that the y variable is not normal, so a prediction interval based on the normal distribution does not make sense. To get an appropriate prediction interval you need some information on what the distribution of the y values is (conditional on the model), but most robust techniques are not based on a specific distribution, just some properties of the distribution. Without some information (or at least an assumption) on that distribution, the predict method cannot create prediction intervals. I know that this does not answer your question, but hopefully helps you to understand what is happening. Think about what your actual scientific question is, it may be that you can answer the question without prediction intervals. If you feel that you really need the prediction intervals, then you will need to do some additional background research into what distribution you think the data comes from, then you can proceed from there. Some options include fitting a model based on that distribution, simulating data from the distribution given the model estimates and the uncertainty in those estimates, quantile regression, mixture of regressions, and others. Hope this helps, -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg ... @imail.orgg 801.408.8111 -----Original Message----- From: r-he ... @r-project.orgg [r-help-bounces@r-project.orgg] On Behalf Of Galkowski, Jan Sent: Wednesday, April 08, 2009 9:32 AM To: r-h ... @r-project.orgg Subject: [R] predict "interval" for lmRob? lm's " predict " function offers an "interval" parameter to choose between 'confidence' and 'prediction' bands. In the package "robust" and for "lmRob", there is also a " predict " but it lacks such a parameter, and the documented "type" parameter has only "response" offerred. Is there some way of obtaining prediction bands from lmRob? Is there an alternative robust (linear) regression package that offers such a capability? Thanks for any and all help. - Jan Galkowski, Akamai Technologies, Cambridge, MA. ______________________________________________ R-h ... @r-project.orgg mailing list PLEASE do read the posting guide and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-h ... @r-project.orgg mailing list PLEASE do read the posting guide and provide commented, minimal, self-contained, reproducible code.
