Solved – Issues with estimating the sparse inverse covariance matrix with Glasso

I am trying to estimate the sparse inverse covariance matrix of my gaussian graphical model. I installed the glasso package in R and tried out some examples.

After that I ran the glasso software on my own data. So I fed it my empirical covariance matrix. However, it seems to get stuck and doesn't give me the results. So I was wondering if I was running the software incorrectly.

I followed this manual:

So if S is my empirical covariance matrix. I just ran the following:

a <- glasso(S, rho=.01, trace=TRUE) # outer loop, m = 1 # outer loop, m = 2 

After the above, it gets stuck.

I am attaching my empirical covariance matrix as well which I feed to the software.

Any guidance will be much appreciated.

Did you standardized data before estimation of covariance matrix? Standardization is necessary before you run glasso, though there may be exceptions ( however, I am not sure about those ocassions). Other option may be estimate covarience matrix and then transform it into correlation matrix. Then run glasso and see the result. Hope it works.

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