I was wondering if it was possible to do a normality test on a very
large sample dataset. It is approx. 160,000 residual estimates from
meshes modelling the brain surfaces of 50 subjects (25 patients).
shapiro.test only works with at most 5000 points. Are there issues with
very large samples
Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Morgan Hough
Sent: Friday, April 13, 2007 3:54 AM
To: [EMAIL PROTECTED]
Subject: [R] normality test for large sample sizes
I was wondering if it was possible to do a normality test on
a very large sample dataset
On 28-Apr-05 Pieter Provoost wrote:
Thanks all for your comments and hints. I will try to
keep them in mind.
Since a number of people asked me what I'm trying to do:
I want to apply Bayesian inference to a simple ecological
model I wrote, and therefore I need to fit (uniform, normal
or
I looked carefully at ?shapiro.test and I did not see it state
anywhere what the null hypothesis is or what a low p-value means. I
understand that I can run the example shapiro.test(rnorm(100, mean =
5, sd = 3)) and deduce from its p-value of 0.0988 that the
null-hypothesis must be normality, but
On 29-Apr-05 roger bos wrote:
I looked carefully at ?shapiro.test and I did not see it state
anywhere what the null hypothesis is or what a low p-value means. I
understand that I can run the example shapiro.test(rnorm(100, mean =
5, sd = 3)) and deduce from its p-value of 0.0988 that the
Hi,
I have a small set of data on which I have tried some normality tests. When I
make a histogram of the data the distribution doesn't seem to be normal at all
(rather lognormal), but still no matter what test I use (Shapiro,
Anderson-Darling,...) it returns a very small p value (which as far
Le 28.04.2005 13:16, Pieter Provoost a écrit :
Hi,
I have a small set of data on which I have tried some normality tests. When I make a histogram of the data the distribution doesn't seem to be normal at all (rather lognormal), but still no matter what test I use (Shapiro, Anderson-Darling,...) it
- Original Message -
From: Romain Francois [EMAIL PROTECTED]
To: Pieter Provoost [EMAIL PROTECTED]; RHELP
R-help@stat.math.ethz.ch
Sent: Thursday, April 28, 2005 2:03 PM
Subject: Re: [R] normality test
Le 28.04.2005 13:16, Pieter Provoost a écrit :
Hi,
I have a small set of data
Romain Francois wrote:
Le 28.04.2005 13:16, Pieter Provoost a écrit :
Hi,
I have a small set of data on which I have tried some normality tests.
When I make a histogram of the data the distribution doesn't seem to
be normal at all (rather lognormal), but still no matter what test I
use
For my money, Frank's comment should go into fortunes. It seems a
rather Sisyphean battle to keep the lessons of robustness on the
statistical table
but nevertheless well worthwhile.
url:www.econ.uiuc.edu/~rogerRoger Koenker
email [EMAIL PROTECTED]
On Thu, 28 Apr 2005 08:52:33 -0500 roger koenker wrote:
For my money, Frank's comment should go into fortunes. It seems a
rather Sisyphean battle to keep the lessons of robustness on the
statistical table but nevertheless well worthwhile.
Added.
On more comment: maybe it's also worth
Subject: Re: [R] normality test
On Thu, 28 Apr 2005 08:52:33 -0500 roger koenker wrote:
For my money, Frank's comment should go into fortunes. It seems a
rather Sisyphean battle to keep the lessons of robustness on the
statistical table but nevertheless well worthwhile.
Added.
On more
Below.
Usually (but not always) doing tests of normality reflect a lack of
understanding of the power of rank tests, and an assumption of high
power for the tests (qq plots don't always help with that because of
their subjectivity). When possible it's good to choose a
robust
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Pieter Provoost
Sent: Thursday, April 28, 2005 7:52 AM
To: R-help@stat.math.ethz.ch
Subject: Re: [R] normality test
Thanks all for your comments and hints. I will try to keep
them in mind
Achim Zeileis wrote:
On Thu, 28 Apr 2005 08:52:33 -0500 roger koenker wrote:
For my money, Frank's comment should go into fortunes. It seems a
rather Sisyphean battle to keep the lessons of robustness on the
statistical table but nevertheless well worthwhile.
Added.
On more comment: maybe
- Original Message -
From: Berton Gunter [EMAIL PROTECTED]
To: 'Pieter Provoost' [EMAIL PROTECTED];
R-help@stat.math.ethz.ch
Sent: Thursday, April 28, 2005 6:26 PM
Subject: RE: [R] normality test
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED
Bert wrote:
You probably have noticed that
I'm quite new
to statistics, but I'm working on that...
And you want to use Bayesian methods?!
I was always under the impression that it's mostly a matter of mindset if
you go Bayesian or frequentist, not of your statistical skills.
[...]
The
I tried to use shapiro.test or ks.test to check the
normality of some data, the problem is, the
distribution function is a mixture of a Gaussian and
some other distributions at the tails. The hypothesis
is that if the tails are excluded, the distribution is
perfect Gaussian, and I want to test
Have you considered Monte Carlo?
Invent a test, then Monte Carlo it to get a p-value.
Something similar to what you are asking is provided by Monte
Carlo confidence bounds on a QQ plot, discussed on this list last June.
To find it, go to www.r-project.org - Search - R
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
Samuel BERTRAND
Doctorant
Laboratoire de Biomecanique
LBM
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
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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[EMAIL PROTECTED] mailing list
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,
Andy and Peter: Of yours, both of you are right.
Re h2g2 (Adams DN 1979):
[42] quite definitely is the answer. I think the problem, to be quite
honest with you, is that you've never actually known what the question is.
[...] So once you do know what the question actually is, you'll know what
Hi Knut,
Knut Unfortunately, a non-significant test is merely
Knut non-conclusive (Popper KR, 1979), so one would have to test for
Knut equivalence, e.g., as TOST (two one-sided tests).
Knut As to whether you can do a Lilliefors test for several groups,
Knut that depends entirely on your
Andy Jacobson [EMAIL PROTECTED] writes:
On the other hand, the context of the citation:
Knut that depends entirely on your ability to understand what the
Knut underlying question would be (see Adams D 1979)
leads me to suspect that you intended to cite The Hitchhikker's
Guide.
Hi,
I use ks.test or lillie.test to verify a normal distribution. It's performed
for a group
My users use SigmaStat software and a One Way ANOVA on several groups
In the result page there is a probability value to determine if Normality
test is failed or passed
So, how can i retrieve this
Hi Laurent,
the answer to your question may be more in the field of statistics than in
the field of R-istics.
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.
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