While I am not a novice when it comes to statistics, this will be the first time I have used R, apart from some intial play. I have normally written my own code for statistical analysis in C++ or fortran, for a number of reasons (in part contingent on what the boss was willing to pay for), and having been programming for a long time, there is no need to spare me the programming details. Just give me an url for a document that explains how to do what I want to do, if there is one.
What I want to do is extract time series data from a database(say, PostgreSQL or MySQL - I routinely use both), analyse it, and put a selection of the statistical results into specific tables. For example, suppose I have daily values for several variates. One thing I might try is to fit a cubic spline to the data, use the spline to obtain an estimate of rate of change and acceleration, and then see if some nonlinear function of the variates can account for a significant percentage of the variation in rate of change and acceleration, perhaps after orthogonalization if there happens to be problems with multicollinearity. Since the data comes in regularly (daily in some cases, weekly in others), I'd want to rerun the whole process at the same interval, without prior analyses messing up a current analysis. I know of plr, for PostgreSQL, but haven't figured out how to use it yet. Is this something I can orchestrate using Perl to tie things together, either using plr or not? If I can use perl for this, a sample perl script showing how to use perl to get data into R and to retrieve statistical output from R into Perl variables would be priceless. What I have read in the preliminary documentation suggests I can, but it is short on detail and directions on where to go next. I'd like to be able to put the master script for this sort of thing into a scheduled task if possible. Thanks Ted ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
