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 > [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.
