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
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