[R] Normality tests

[Frank E Harrell Jr]

Alexandre Christie wrote:
> Hello All,
> 
> 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?
> 
> My team and I would be very grateful for your advice on this.
> 
> Yours sincerely,
> 
> Alex.

Alex it would be good to work closely with a local statistician.  Models 
do not require raw data to be normally distributed, only residuals or 
conditional distributions.  And there are many models that don't require 
strong distributional assumptions (e.g. Cox regression, proportional 
odds model, transform-both-sides generalized additive models).

Frank

-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University