Comments (3)
I can see how that statement is confusing. Nonetheless, it seems accurate. The red curve indicates convergence, while blue does not. That the red cross map value (at full library size) is greater than cross correlation is not evidence of significance, but, that is not claimed. As mentioned, some people take that difference as a sign, perhaps even quantitative metric, of non linearity. This is what was done in the previous version of rEDM, so it remains in the current tutorial.
Personally, I feel that the cross map correlation difference can only be viewed as a potential indicator, since linear correlation can over/under predict when the data are non linear, Anscombes quartet. A better metric would be a mutual information nonlinearity discrimination. But, this comparison to linear correlation still has some value as an illustration of the Type III error presuming linearity where it should not be applied.
I'm sure others will have different opinions.
Rate of convergence: that's an interesting question, I'm not aware of validated studies that relate the rate of convergence with cross map "strength". My understanding is to use the value at the full library size, or, the value at which the cross map metric has converged.
What I take from Sugihara et al. is that convergence arises if the variables share simplex predictive power, shared state dynamics, as the library of states of provides more complete information of the system dynamics. Trying to say something about the "rate" of convergence is saying something about an incomplete, partial, view of the system dynamics. I could see that having value in perhaps assessing "how complete" information regarding an observed variable is represented in the library of states. Perhaps someone with a deeper understanding can clarify this...
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The above picture is from this page of tutorial. https://ha0ye.github.io/rEDM/articles/rEDM.html#convergent-cross-mapping-ccm
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Thanks for your detailed explaination!
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Related Issues (20)
- The Multiview function does not select the best views for the average prediction HOT 2
- Multiview() chooses the maximum lag based on the number of predictor columns used, regardless of E. HOT 2
- Prediction not NA with disjoint/segmented pred HOT 1
- setting knn < E+1 HOT 7
- dgelss failed HOT 5
- make_block producing zeros instead of NaN HOT 4
- When should different surrogate generation functions be used? HOT 1
- error in installation
- Example of allowed libSizes for tau or negative tp HOT 4
- Tp < -1 gives error HOT 3
- Different sampling between legacy and current version? HOT 4
- can not library the "rEDM" HOT 22
- WARNING: ComputeError(): nan predictions found error not computed. HOT 11
- CCM Parallel Processing
- Prediction of single time points HOT 1
- Inverted name for the output generated by block_lnlp function HOT 13
- R 4.2.0 Character Encoding on Windows: Can not load rEDM HOT 5
- version 1.15 dealing with NaN HOT 4
- Questions about 'Nonlinearity' and the significance of ρ under the largest L. HOT 1
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