Thanks to everyone for the responses!
Samuel

>  SUMMARY :

MY QUESTION :

>>Hello,
>>
>>I am analysing several samples whose sizes are from 9 to 110.
>>I would like to test their distribution with R,
>>whether they are normal or not.
>>I wonder which test for normality from R should I use .
>>
>>Thank you for help.
>>
>>Samuel BERTRAND
>>Doctorant
>>Laboratoire de Biomecanique
>>LBM - ENSAM - CNRS UMR 8005
>>151, bd de l'Hopital
>>75013 PARIS
>>Tel. +33 (0) 1 44 24 64 53
>>Fax. +33 (0) 1 44 24 63 66

THE RESPONSES :

A qqplot is a good raw test to look quickly
the normality of a distribution.
best
A.S.

----------------------------

Alessandro Semeria
Models and Simulations Laboratory
Montecatini Environmental Research Center (Edison Group),
Via Ciro Menotti 48,
48023 Marina di Ravenna (RA), Italy
Tel. +39 544 536811
Fax. +39 544 538663
E-mail: alessandro.semeria@cramont.it

=====================================================================

shapiro.test
is also relevant.
-- 
Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page: http://www.sas.upenn.edu/~baron
R page: http://finzi.psych.upenn.edu/

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You can test normality with the Jarque-Bera test. You can get this test in
the package tseries, of Adrian Trapletti. The package is in CRAN, and you
can install it directly from within R.
Hope it will help you.
Agustin

"RCU" <aalonso@rcumariacristina.com>


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Hello Samuel,
Regardless of some more fundamental problems (see below), a test to "prove" 
normality based on a sample of 9? - Fugetaboutit.
Knut
At 10:20 2004-02-06 +0100, I wrote:
...
It may be tempting to interpret a non-significant result of a statistical 
test as to verify the hypothesis, e.g., as to verify that the distribution 
of the data is Gaussian. Unfortunately, a non-significant test is merely 
non-conclusive (Popper KR, Wien: 1937), so one would have to test for 
equivalence, e.g., as TOST (two one-sided tests). To do this with the a 
test for normality (Shapiro, Lillifors, ...), however, it may be difficult 
to come up with a justification for an equivalence limit.
...
"Knut M. Wittkowski" <kmw@mail.rockefeller.edu>

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