[R] Factor Analysis

[Spencer Graves]

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.
>>
>