I'm sorry, I pressed a wrong button and sent an incomplete answer. Below follows the completed e-mail.
> I am new to R, and I am writing to seek your advice on how best to use it to > run > R's various normality tests in an automated way. > > In a nutshell, my situation is as follows. I work in an investment bank, and > my > team and I are concerned that the assumption we make in our models that the > returns of assets are normally distributed may not be justified for certain > asset classes. We are keen to check this statistically. > > To this end, we have an Excel document which contains historical data on the > returns of the asset classes we want to investigate, and we would like to run > R's multiple normality tests on these data to check whether any asset classes > are flagged up as being statistically non-normal. > > I see from the R documentation that there are several R commands to test for > this, but is it possible to progamme a tool which can (i) convert the Excel > data > into a format which R can read, then (ii) run all the relevant tests from R, > then (iii) compare the results (such as the p-values) with a user-defined > benchmark, and (iv) output a file which shows for each asset class, which > tests > reveal that the null hypothesis of normality is rejected? The short answer is `yes, this is perfectly possible' by putting all the pieces in an R script file and sourcing it or processing it in batch mode. ad (i): there are several ways of accessing Excel files. Using RODBC is one of them. Section 8 of the R Data Import / Export gives an overview of all options. http://cran.r-project.org/doc/manuals/R-data.html#Reading-Excel-spreadsheets Here's a simple example for RODBC: library(RODBC) z <- odbcConnectExcel("rexceltest.xls") dd <- sqlFetch(z, "Sheet1") close(z) ad (ii): this is a matter of conducting the tests and storing (what you would like to keep from) the test results in an appropriate data structure. ad (iii): should be straightforward as well. ad (iv): you did not specify the output format, but R could write to a.o. a text file, an HTML file, a LaTeX file and if needed an Excel file. Relevant packages include xtable, R2HTML and rcom. HTH, Tobias P.S. It is always a good idea to define small functions for each step in the process and then use these in the function definition of one big function that would be something like checkAssetNormality(file = "myassets.xls, otherarg1, otherarg2, outfile = "res_myassets.html", outdir = ".") P.P.S. R has very neat and powerful graphical capabilities. It is quite easy to rapidly produce large grids of QQ-plots for all the assets concerned. This would give you additional information about the nature of the deviation from normality. > My team and I would be very grateful for your advice on this. > > Yours sincerely, > > Alex. > > ______________________________________________ > R-help@stat.math.ethz.ch 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. > > ______________________________________________ R-help@stat.math.ethz.ch 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.