Many apologies for the poor steer; you are quite right.
'fraid I hit 'send' before double-checking the help page myself. Next time...
S
>>> Gavin Simpson 16/02/2009 10:59 >>>
On Mon, 2009-02-16 at 10:45 +, S Ellison wrote:
> princomp uses the raw data and calculates the correlation or
> co
On Mon, 2009-02-16 at 10:45 +, S Ellison wrote:
> princomp uses the raw data and calculates the correlation or
> covariance matrix on the way to the PC's, so that doesn't use a
> correlation matrix itself. You do, however, get the choice.
That *isn't* what princomp() does. If you supply a vali
sqrt(svd(x)$d) maybe 2 more operations than princomp(covmat=x), but it
is hardly a chore.
On Feb 16, 9:15 pm, Mark Difford wrote:
> Hi Glen, Andrew,
>
> >> The PCA is just a singular value decomposition on a sample covariance/...
>
> I believe that Bjørn-Helge Mevik's point was that __if you read
princomp uses the raw data and calculates the correlation or covariance matrix
on the way to the PC's, so that doesn't use a correlation matrix itself. You
do, however, get the choice.
However, PC's are the eigenvectors of the correlation (or covariance) matrix,
so in principle calling eigen()
Hi Glen, Andrew,
>> The PCA is just a singular value decomposition on a sample covariance/...
I believe that Bjørn-Helge Mevik's point was that __if you read the
documentation__ you will see the argument "covmat" to princomp(). This,
really, is much more straightforward and practical than Andrew
The PCA is just a singular value decomposition on a sample covariance/
correlation matrix. Do a search for ?svd and get the eigenvalues and
vectors from that function.
On Feb 14, 10:30 am, "glenn" wrote:
> Hi All, would appreciate an answer on this if you have a moment;
>
> Is there a function (
"glenn" writes:
> Is there a function (before I try and write it !) that allows the input of a
> covariance or correlation matrix to calculate PCA, rather than the actual
> data as in princomp()
Yes, there is: princomp(). :-)
--
Bjørn-Helge Mevik
_
7 matches
Mail list logo
