On Saturday, January 26, 2013 10:22:54 PM UTC+1, M Edward Borasky wrote:The strategy is simple:[...]Ah, I see. I was thinking about a way to use a linear fit to predict X(1) while youused a nonliner fit for the whole equation including sigma and kappa.It looks like nls() can solve these and there is also an option to add constraints.Talking about different formulas, here is one for DrQ:On www.teamquest.com under Resources->White Papers there is a paper'Reevaluating "Evaluating Scalability Parameters: A Fitting End"'. It includesan adoption to the way sigma and kappa are calculated and states that thecalculation leads to a better match between measurements and calculated fit.Any thoughts on that?Regards,Stefan
I did some more work on the package.
The summary now includes some statistics about the distrubution of the efficiency. There is also the method efficiency() to extract the values from the model.
Nonlinear regression is implemented with two different algorithms. One uses the standard nls() function and one is build upon the nlmrt package. The author of that package is the already mentioned John C. Nash. I expect it to be more robust than the standard nls() function.
There is certainly room for improvement but I believe the package can already be used for some analysis work. Therefore I would like to submit the package to CRAN and make it available to a broader audience.
Since the work is derived from GCaP I need the confirmation that the distribution of the source code does not violate any copyright/license statements. So to comply with the CRAN policy I would like to ask if there are any objections to this plan.
Release 1.1.0 of my usl package has just appeared on CRAN.It may take another day until your mirror has all the binaries.The update has only a small functional change. The efficiency() function now returns a named vector to show which efficiency value corresponds to which load value.The main reason for the update is the vignette for the package. It includes step-by-step examples and should help with the general analysis approach.After installation of the updated package you can access the vignette with the following R command:R> vignette("usl")The vignette is also accessible from the package page on http://cran.r-project.org/web/packages/usl/index.html