PC running slow?
You may have come across an error message saying that the solve.defaultsigma system bug is computationally unique. Coincidentally, there are several ways to solve this problem, and we will do it shortly. This means that your version matrix is not reversible and for these reasons cannot be used to develop a meaningful regression model. This follows everywhere from linearly dependent columns, i.e. from strongly correlated variable stars.
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Your design matrix is not reversible, so it cannot be used to develop a regression model. This final result is derived linearly from the dependent columns, i.e. remarkably correlated variables.s. Look at the pairwise covariance (or correlation) of your variables when you need to determine if items can be removed. You’re looking for covariance correlations) (or >>0. Alternatively, you can probably make this variable choice automatically using the latest stepwise regression.
It could also be because you have more variables than research, in which case your architecture matrix is probably not complete. It’s a little harder to spot, but there are ways. I’m sure lasso regression should help if you want data to be “wide” instead of “long”.
Remember that when you decide to use lasso or incremental selection, you are doing a lot more (in terms of move selection) than just dealing with multicollinearity.
answered November 13, 2013 at 7:26 pm.
David MarksDavid Marks
What is computationally singular matrix?
A completely unique system is one in which some of the columns are linear combinations on top of each other; In this case, the principal matrix has no inverse at all.
I had exactly the same problem. My teacher told me that this is because one of my variables is much larger than the others. (At best, trading volume was much larger than the pair’s profits at different times.) The problem is with the limitations of floating point calculations and therefore precision, not something mathematical, also called statistics.
data$Volume <- data$Volume 1000000 each
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