[R] singular factor analysis

[Spencer Graves]

Hi, Patrick:  Thanks very much.  I'll try it.  Spencer Graves

Patrick Burns wrote:
> This is a very common computation in finance.
>
> On the public domain page of the Burns Statistics website
> in the financial part is the code and R help file for
> 'factor.model.stat'.  Most of the complication of the code
> is to deal with missing values.
>
> Patrick Burns
> patrick at burns-stat.com
> +44 (0)20 8525 0696
> http://www.burns-stat.com
> (home of S Poetry and "A Guide for the Unwilling S User")
>
> Spencer Graves wrote:
>
>>      Are there any functions available to do a factor analysis with 
>> fewer observations than variables?  As long as you have more than 3 
>> observations, my computations suggest you have enough data to 
>> estimate a factor analysis covariance matrix, even though the sample 
>> covariance matrix is singular.  I tried the naive thing and got an 
>> error:
>> > set.seed(1)
>> > X <- array(rnorm(50), dim=c(5, 10))
>> > factanal(X, factors=1)
>> Error in solve.default(cv) : system is computationally singular: 
>> reciprocal condition number = 4.8982e-018
>>
>>      I can write a likelihood for a multivariate normal and solve it, 
>> but I wondered if there is anything else available that could do this?
>>      Thanks,
>>      Spencer Graves
>>
>> ______________________________________________
>> R-help at stat.math.ethz.ch mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide 
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>>  
>>