On Fri, Jun 04, 2004 at 11:50:32PM +1200, Ko-Kang Kevin Wang wrote: > Although it has a slightly higher learning curve than SPSS-like > program, it gets easier to use once one is familiar with it. One of > the main advantage it has over SPSS-like software is that you do not > need to explicitly create dummy variables. You only need to specify > your dependent variable and independent variables and R will fit it > (and create dummy variables automatically) for you.
I think that even if the above is an advantage when compared to SPSS, it is more of a minor, convenient feature than one of the major advantages of R. Even though I majored in finance, at the moment I consider myself to be more of a macroeconomist than a "finance person". I wrote a lot of my finance calculations in R (without using Rmetrics, as this was two years ago and I did not know about Rmetrics then), including derivatives pricing, binomial trees and term structure models. I would emphasize the following: 1. R is free, both in the sense of "gratis" and "libre". The latter is more important in this context, as "finance people" can usually afford the price of software, but IMO free software often means better quality (this applies to R for sure). 2. The programming language is really friendly and convenient to work with. In finance, you often need to hack together special solutions for problems that are not conventional (especially in term structure models, but I think that the same applies to bi- and trinomial models and their ilk). As an R newbie, it took me an afternoon to implement a basic toolkit for the former, which I could use for interesting explorations. 3. Advanced graphing packages. This is quite important, visualization is often the key in finance models -- sometimes one doesn't notice that the model is wrong until one sees a graph (eg a term structure model with unexplained "breaks" in the interest rate curve). 4. Interface to databases, eg Oracle and MySQL. Building certain types of models requires access to huge amounts of data (eg credit scoring systems). 5. Tons of statistical functions. For example, R has all the tools one needs to build very sophisticated credit scoring systems (note that banks for often pay thousands of dollars for commercial versions of software used for this purpose, I am baffled about this). Hope this helps, Tamas -- Tamás K. Papp E-mail: [EMAIL PROTECTED] Please try to send only (latin-2) plain text, not HTML or other garbage. ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html