2,684
edits
XSCALE ISOCLUSTER: Difference between revisions
Jump to navigation
Jump to search
→Notes
(→Usage) |
(→Notes) |
||
| Line 67: | Line 67: | ||
== Notes == | == Notes == | ||
* For meaningful results, the number of known values [N*(N-1)/2 is the number of pairwise correlation coefficients] should be (preferrably much) higher than the number of unknowns (1+n*(N-1)). This means that one needs at least 5 data sets if dim=2, and at least 7 if dim=3. | * For meaningful results, the number of known values [N*(N-1)/2 is the number of pairwise correlation coefficients] should be (preferrably much) higher than the number of unknowns (1+n*(N-1)). This means that one needs at least 5 data sets if dim=2, and at least 7 if dim=3. | ||
* The clustering of data sets in a low-dimensional space uses the method of Rodriguez and Laio (2014) ''Science'' '''344''', 1492-1496. | * The clustering of data sets in a low-dimensional space uses the method of Rodriguez and Laio (2014) ''Science'' '''344''', 1492-1496. The clustering result should be checked by the user; one should not rely on this to give sensible results! The main criterion for a cluster should be that all data sets in it are in the same or similar direction, when seen from the origin ("0" in coot) - the length of each vector is not important since it is ''not'' related to the amount of non-isomorphism, but to the strength of the data set. | ||
* The eigenvalues are printed out by the program, and can be used to deduce the proper value of the required dimension n. To make use of this, one should run with a high value of dim (e.g. 5), and inspect the list of eigenvalues with the goal of finding a significant drop in magnitude (e.g. a factor of 3 drop between the second and third eigenvalue would point to the third eigenvector being of low importance). | * The eigenvalues are printed out by the program, and can be used to deduce the proper value of the required dimension n. To make use of this, one should run with a high value of dim (e.g. 5), and inspect the list of eigenvalues with the goal of finding a significant drop in magnitude (e.g. a factor of 3 drop between the second and third eigenvalue would point to the third eigenvector being of low importance). | ||
* A different but related program is [[xds_nonisomorphism]]. | * A different but related program is [[xds_nonisomorphism]]. | ||