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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