Don't you have the dimension of x backward? Try:
set.seed(1) x <- matrix(rnorm(50, 3, 3), 10, 5) vinv <- solve(crossprod(x)) vinv
[,1] [,2] [,3] [,4] [,5] [1,] 0.019918251 -0.006247646 0.006600209 0.003687249 -0.018670806 [2,] -0.006247646 0.018121025 -0.014815905 -0.005647350 0.003434065 [3,] 0.006600209 -0.014815905 0.023411617 -0.002250342 -0.003258960 [4,] 0.003687249 -0.005647350 -0.002250342 0.025168959 -0.020070844 [5,] -0.018670806 0.003434065 -0.003258960 -0.020070844 0.039593016 If you really have 5 cases and 10 variables, the covariance matrix will have to be singular. Andy
From: Marc Bernard
Dear All,
I have written the following programs to find a
non-singular (10*10) covariance matrix.
Here is the program:
nitems <- 10
x <- array(rnorm(5*nitems,3,3), c(5,nitems))
sigma <- t(x)%*%x
inverse <- try(solve(sigma), TRUE)
while(inherits(inverse, "try-error"))
{
x <- array(rnorm(5*nitems,3,3), c(5,nitems))
sigma <- t(x)%*%x
inverse <- try(solve(sigma), TRUE)
}
The loop doesn't stop ... This means that no "non-singular"
matrix was found!!!
some thing wrong !!
Thanks a lot for any reply
Bernard
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