[R] Factor Analysis
Spencer Graves
spencer.graves at pdf.com
Thu Feb 27 20:12:03 CET 2003
Of course. Thanks for the correction. Spencer Graves
ripley at stats.ox.ac.uk wrote:
> On Thu, 27 Feb 2003, Spencer Graves wrote:
>
>
>>To obtain an nonsingular estimate of an (n x n) covariance or
>>correlation matrix, you need at least (n+1) observations. However, you
>>can obtain estimates of the largest k singular values or eigenvalues
>>with only (k+1) observations. The principal components routine must use
>>something like "eigen" or "svd", which does not require a nonsingular
>>covariance matrix.
>
>
> That's because principal components analysis is defined for simgular
> covariance matrices, but the factor analysis model can never generate
> them. It's not to do with the computational technique.
>
> Using PCA to find constant combinations is quite common, and such data
> matrices have singular covariance structures.
>
>
>>rahul.maniar at feri.de wrote:
>>
>>>I am encountering a problem while doing factor analysis in R. I am using
>>>correlation matrix of the performance data of funds.And it gives me error
>>>message saying singular matrix in use. Now when I try to find the
>>>determinant of this matrix it is indeed singular. The problem is when I use
>>>same matrix for principal component analysis it works. I was wondering if
>>>any of you could help me with this.
>>
>
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