[R] Normality tests

Frank E Harrell Jr
Sat, 04 Aug 2007 16:30:08 -0000

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