[R] How to find the variance-covariance matrix of a random-vector using R

[Bill Dunlap]

?var

E.g.,

> x <- mvtnorm::rmvnorm(1e5, mean=101:105, sigma=matrix(1,5,5)+diag(11:15))
> dim(x)
[1] 100000      5
> var(x)
           [,1]       [,2]       [,3]       [,4]      [,5]
[1,] 11.9666055  1.0603876  0.9627672  1.0371084  0.983217
[2,]  1.0603876 13.0774518  1.0228972  0.9261868  1.059799
[3,]  0.9627672  1.0228972 13.9296063  1.0444007  1.051089
[4,]  1.0371084  0.9261868  1.0444007 15.1556199  1.052573
[5,]  0.9832170  1.0597985  1.0510888  1.0525734 15.965351

-Bill

On Tue, Sep 20, 2022 at 4:24 AM Sun, John <jsun20 using albany.edu> wrote:

> Dear All,
> Reposting as plain text rather than html.
>
> I realized that R does not support finding the variance-covariance matrix
> of a random-vector. It must take two arguments. Numpy's cov doesn't give
> sensible results.
> I ask in a bigger context of finding the variance-covariance matrix of the
> vector of the dependent variables per subject which is the covariance form
> of the working-correlation matrix in GEE by Liang-Zeger (1986). Knowing it
> gives me better inference via efficiency improvement.
>
> I have not received a reply on these posts, so I ask.
>
> https://stats.stackexchange.com/questions/589022/how-to-find-covy-i-using-software-in-the-context-sum-i-1-mathrmk
>
> https://stackoverflow.com/questions/73755242/is-there-a-r-function-or-python-for-finding-the-covariance-matrix-of-a-random-ve
>
> Best regards,
> Kpjm
>
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