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 [EMAIL PROTECTED] +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 > >______________________________________________ >[email protected] 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. > > > > ______________________________________________ [email protected] 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.
