Michael, Mikhail Many thanks for your helpful comments. My faith in community support continues to grow.
Michael: I'm looking to use some sort of flexible spline-like fit (smooth.spline, lowess etc). Many thanks for sharing your expertise. I actually cross posted this on to the "manipulatr" google group, here is the response from Peter Meilstrup: " For (1) you might want to take a look at rollapply() and related functions in the zoo package. for (2), don't put the different samples of your curve fit into different columns. Instead imagine generating a data frame with three columns: bae.date (date each your fit is based around) prediction.date (date you are extrapolating to) preciction (the fitted value) so if you have 100 dates, and generate a 7 point curve from each date, you end up with 700 rows." As ever time pressures kind of dictate that I start from what I know. I've only pretty basic database skills at the moment, so will try zoo/TTR first and try PostgreSQL if that isn't satisfactory. -----Original Message----- From: Mikhail Titov [mailto:m...@gmx.us] Sent: 12 July 2012 00:22 To: R. Michael Weylandt Cc: Russell Bowdrey; r-help@r-project.org Subject: Re: do I need plyr, apply or something else? "R. Michael Weylandt" <michael.weyla...@gmail.com> writes: > On Wed, Jul 11, 2012 at 10:05 AM, Russell Bowdrey > <russell.bowd...@justretirement.com> wrote: >> >> Dear all, >> >> This is what I'd like to do (I have an implementation using for >> loops, which I designed before I realised just how slow R is at >> executing them - this process currently takes days to run). >> >> I have a large dataframe containing corporate bond data, columns are: >> BondID >> Date (goes back 5years) >> Var1 >> Var2 >> Term2Maturity >> >> What I want to do is this: >> >> 1) For each bond, at each given date, look back over 1 year and append >> some statistics to each row ( sd(Var1), cor(Var1,Var2) over that year etc) >> > > Look at the TTR package and the various run** functions. Much faster. > >> a. It seems I might be able to use ddply for this, but I can't work >> out how to code the stats function to only look back over one year, >> rather than the full data range >> >> b. For example: dfBondsWithCorr<-ddply(dfBonds, .(BondID), >> transform,corr=cor(Var1,Var2),.progress="text") >> returns a dataframe where for each bond it has same corr for each >> date >> >> 2) On each date, subset dfBondsWithCorr by certain qualification >> criteria, then to the qualifiers fit a regression through a Var1 and >> Term2Maturity, output the regression as a df of curves (say for each >> date, a curve represented by points every 0.5 years) >> >> a. I can do this pretty efficiently for a single date (and I suppose >> I could wrap that in a function) , but can't quite see how to do the >> filtering and spitting out of curves over multiple dates without >> using for loops >> > > This ones harder. For simple linear regressions, you can solve the > regression analytically (e.g., slope = runCov / runVar and mean > similarly) but doing it for more complicated regressions will pretty > much require a for loop of one sort or another. Can you say what sort > of model you are looking to use? > >> Would appreciate any thoughts, many thanks in advance I feel like PostgreSQL will do the work better. It has support for basic statistics [1] and you can use window functions [2] to limit the scope for last year only. Then you get your data with RODBC or something. I suspect you have you data in some sort of DB in the first place. Perhaps it has similar features. [1] http://www.postgresql.org/docs/9.1/static/functions-aggregate.html#FUNCTIONS-AGGREGATE-STATISTICS-TABLE [2] http://www.postgresql.org/docs/9.1/interactive/sql-expressions.html#SYNTAX-WINDOW-FUNCTIONS -- Mikhail This email and any attachments are confidential and inte...{{dropped:29}} ______________________________________________ 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.