You could also check this function I implemented awhile back:
http://www.fernandohrosa.com.br/en/P/sphericity-test-for-covariance-matrices-in-r-sphericity-test/
On Fri, Jun 17, 2011 at 4:43 PM, thibault grava wrote:
> Hello Dear R user,
>
> I want to conduct a Principal components analysis and I
Sorry for the confusion, thank you for the correction and explanation.
Tal
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Thanks for the clarification. That makes sense.
To summarize, bartlett.test() in the base distribution of R is not the
sphericity test, and it relies on the higher moments of the normal
distribution. The sphericity test can be computed with the formula provided
or the one implemented in the psych
On Jun 18, 2011, at 15:48 , Tal Galili wrote:
> Hi Thibault,
> Not that I think you'll use this after the above responses, but for the
> record, have a look at:
> ?bartlett.test
That's the "other" Bartlett's test, namely the one for comparison of variances.
That test is well known to rely on hi
I do not understand why that would be the case as the only input involving
the relationship of the data is the determinant of the correlation matrix.
For what you suggest to be true, the non-normality of the data would have
to introduce correlation.
If what you are saying is true, we would expect
Hi Thibault,
Not that I think you'll use this after the above responses, but for the
record, have a look at:
?bartlett.test
Cheers,
Tal
Contact
Details:---
Contact me: tal.gal...@gmail.com | 972-52-7275845
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Yes, Bartlett's is not a good way to "justify" a PCA.
David Cross
d.cr...@tcu.edu
www.davidcross.us
On Jun 18, 2011, at 1:47 AM, Jeremy Miles wrote:
> cortest.bartlett() in the psych package.
>
> I've never seen a non-significant Bartlett's test.
>
> Jeremy
>
>
>
> On 17 June 2011 12:43,
On Jun 18, 2011, at 10:48 , (Ted Harding) wrote:
> To add to Jeremy's comment below: The Bartlett test is very
> sensitive to non-normality in the data, so can readily give
> "significant" results even for non-correlated data.
Hmm, I wouldn't bet on that. Correlation tests are usually fairly rob
To add to Jeremy's comment below: The Bartlett test is very
sensitive to non-normality in the data, so can readily give
"significant" results even for non-correlated data.
Ted.
On 18-Jun-11 06:47:52, Jeremy Miles wrote:
> cortest.bartlett() in the psych package.
>
> I've never seen a non-signifi
cortest.bartlett() in the psych package.
I've never seen a non-significant Bartlett's test.
Jeremy
On 17 June 2011 12:43, thibault grava wrote:
> Hello Dear R user,
>
> I want to conduct a Principal components analysis and I need to run two
> tests to check whether I can do it or not. I found
The formula for the chi-square value is:
-( (n-1) - (2*p-5)/6 )* log(det(R))
where n is the number of observations, p is the number of variables, and R
is the correlation matrix. The chi square test is then performed on
(p^2-p)/2 degrees of freedom. So you can compute it by hand. Or you can use
t
Hello Dear R user,
I want to conduct a Principal components analysis and I need to run two
tests to check whether I can do it or not. I found how to run the KMO
test, however i cannot find an R fonction for the Bartlett's test of
sphericity. Does somebody know if it exists?
Thanks for your hel
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