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Why is the r-error system is computationally singular?
The problem is because of limitations in floating-point computations and precision, not anything inherently mathematical or statistical. Therefore I’ve divided the biggest variable by 1000000. Highly active question. Earn 10 reputation (not counting the association bonus) in order to answer this question.
How to calculate the reciprocal condition number in solve ( )?
Thanks! In solve (), use a smaller tolerance, like solve (…, tol = 1e-20) . This should be fine since you get reciprocal condition number = 1.71139e-19 . More info in the help file and this related question. Thanks for contributing an answer to Stack Overflow!
Why do I get the error message rnorm is singular?
I get the error message: I have been trying to figure out what is the reason behind the problem, and other threads at Stack Overflow have suggested the issue might be due to singular matrices, highly correlated variables, linear combination etc. However, I assumed that rnorm would avoid the mentioned problems.
What is the reciprocal condition number in Vamos?
This should be fine since you get reciprocal condition number = 1.71139e-19 . More info in the help file and this related question. Thanks for contributing an answer to Stack Overflow!
What is the error in solve.default ( Sigma )?
Error in solve.default (Sigma) : system is computationally singular: reciprocal condition number = 0 foo_ts is a time series data. I have tried to adapt examples in stackexchange and stackoverflow but have not been successful in running the VAR model.
What is the error in solve.default ( Vamos )?
Error in solve.default(vamos): system is computationally singular: reciprocal condition number = 1.71139e-19 Error in solve.default(vamos): system is computationally singular: reciprocal condition number = 1.71139e-19.
Why is there a problem with r-error?
(In my case, the trading volume was much bigger than the returns in two different moments.) The problem is because of limitations in floating-point computations and precision, not anything inherently mathematical or statistical. Therefore I’ve divided the biggest variable by 1000000.
Why do I get a singular fit in the lme4 model?
I’m running a varying intercepts varying slopes multilevel model with the lme4::lmer () function with no group level predictors and only one predictor: FilingFee to predict evictionfilingrate. I cannot for the life of me figure out why I am getting a singular fit and correlation of -1 between the random effects intercept and slope.