Timeline for Why is the estimated covariance matrix by glasso always zero? (approx = TRUE)
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| Aug 7 at 7:40 | history | edited | Frans Rodenburg | CC BY-SA 4.0 |
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| Apr 23, 2016 at 17:08 | history | edited | user4704857 | CC BY-SA 3.0 |
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| Apr 23, 2016 at 17:02 | comment | added | user4704857 |
I just ran glasso(var(X), rho = 0.06, penalize.diagonal=F, approx=TRUE) which zero=NULL as default value. Estimated cov matrix is still zero. I have added a link to download variable X to the end of the post.
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| Apr 23, 2016 at 17:00 | history | edited | user4704857 | CC BY-SA 3.0 |
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| Apr 23, 2016 at 16:44 | comment | added | Mark L. Stone | What happens when you set zero = NULL (i.e., not specify zero)? Sorry, I don't know what zeros you have specified when you do zero = info[-c(1000:1950000),]. Are you leaving out your first 999 constraints (whatever they are)? | |
| Apr 23, 2016 at 16:37 | comment | added | user4704857 |
@MarkL.Stone Thanks. The weird thing is that even by running glasso(var(X), rho = 0.01, zero = info[-c(1000:1950000),], approx=TRUE), the estimated cov matrix is still zero!!
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| Apr 23, 2016 at 16:18 | comment | added | Mark L. Stone | First of all, per the documentation, each element (k,j) is constrained to be zero if (j,k) is constrained to be zero. Does that still leave you with 5300 elements not constrained to be zero? Second of all, however many unconstrained elements you think that leaves you, I presume the estimated matrix must be positive semi-definite (PSD). The elements constrained to be zero can indirectly constrain other elements of the matrix due to the PSD constraint. Are you constraining any diagonal elements to be zero? I think you will find the specification of elements constrained to be zero to be key. | |
| Apr 23, 2016 at 16:05 | comment | added | user4704857 | @MarkL.Stone Indeed I force the algorithm to make most of the entries zero and only about 5300 entries are non-zero which I would like to estimate. | |
| Apr 23, 2016 at 16:01 | comment | added | Mark L. Stone | I don't know anything about this, but a quick perusal of cran.r-project.org/web/packages/glasso/glasso.pdf , shows that your 'info' is being used to specify entries in the estimated (inverse) covariance matrix which are constrained to be zero. Are you constraining all entries to be zero, or at least enough of them that all zeros is the only solution it can find? What are the contents of 'info'? | |
| Apr 23, 2016 at 15:39 | history | asked | user4704857 | CC BY-SA 3.0 |