Comments (9)
Can you prepare a reproducible example? If not, can you reproduce this in non-parallel run?
Non-parallel runs allow better tracing: there are now 60 calls to chol
in 12 functions that can give that very same error message, and we have hard time guessing which of them is involved here. Now clusterApplyLB
is the last command we have written and tracing ends there from our point of view.
Yes, this can happen, and issue #45 indeed has similar features.
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I tried running a non-parallel run and got this error:
Computing chain 1
Hmsc::computeInitialParameter - initializing fixed effects with SSDM estimates
Error in chol.default(V) :
the leading minor of order 4 is not positive definite
In addition: Warning messages:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred
2: glm.fit: fitted probabilities numerically 0 or 1 occurred
3: glm.fit: fitted probabilities numerically 0 or 1 occurred
4: glm.fit: fitted probabilities numerically 0 or 1 occurred
5: glm.fit: fitted probabilities numerically 0 or 1 occurred
6: glm.fit: fitted probabilities numerically 0 or 1 occurred
7: glm.fit: fitted probabilities numerically 0 or 1 occurred
8: glm.fit: algorithm did not converge
9: glm.fit: fitted probabilities numerically 0 or 1 occurred
10: glm.fit: fitted probabilities numerically 0 or 1 occurred
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Seems to be the "unexplained variance in species 'niches' (linear coefficients)" that cannot be inverted. It could be that failures in glm
fit are a part of the problem. @ovaskain?
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@ovaskain We have similar reports when initPar="fixed effects"
was used (issue #45). Now we also have warnings from glm
. These seem to be cases with complete separation or other extreme cases where binary models tend to either 0 or 1, or Poisson model to 0 which cannot be represented with the link functions, and the linear predictor tends towards minus or plus infinity. It won't go that far, but the initial values of linear predictors will be extreme, and as initial values the MCMC chains can wander off to impossible territories. I don't know if we should try to handle these cases that give a warning glm.fit: fitted probabilities numerically 0 or 1 occurred
warning?
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Thank you for your help! This seems to have fixed the issue.
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Hi again,
I've since rerun a few models that did not have any error when initPar= "fixed effects" was set and have run them without this. I've noticed that in many cases the Tjur R2 values, AUC, and WAIC results have changed quite a lot (For example, one model that originally had a mean Tjur R2 of .40 and was initially my best fitting model overall dropped to 0.32).
Any idea why this may have occurred?
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