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Cc analysis: Difference between revisions
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# as long as the problem is well-determined, the vectors can be calculated. Unknown CCs between data sets (e.g. in case of crystallographic data sets that don't have common reflections) can be estimated from the dot product of their vectors. Well-determination means: each data set has to be related (directly or indirectly i.e through others) to any other by at least as many CCs as the desired number of dimensions is. A necessary condition for this is that each data set has at least as many relations to others (input lines to cc_analysis involving this data set) as the number of dimensions is. It is of course better if more relations are specified! | # as long as the problem is well-determined, the vectors can be calculated. Unknown CCs between data sets (e.g. in case of crystallographic data sets that don't have common reflections) can be estimated from the dot product of their vectors. Well-determination means: each data set has to be related (directly or indirectly i.e through others) to any other by at least as many CCs as the desired number of dimensions is. A necessary condition for this is that each data set has at least as many relations to others (input lines to cc_analysis involving this data set) as the number of dimensions is. It is of course better if more relations are specified! | ||
== The program == | == The Fortran program == | ||
<code>cc_analysis</code> calculates the vectors from the pairwise correlation coefficients. The (low) dimension must be specified, and a file with lines specifying the correlation coefficients must be provided. | <code>cc_analysis</code> calculates the vectors from the pairwise correlation coefficients. The (low) dimension must be specified, and a file with lines specifying the correlation coefficients must be provided. | ||
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<input.dat> has lines with items: i j corr [ncorr] | <input.dat> has lines with items: i j corr [ncorr] | ||
-b option: <input.dat> is a binary file (4 bytes for each item) | -b option: <input.dat> is a binary file (4 bytes for each item) | ||
-w option: calculate weights from | -w option: calculate weights from number of correlated items (4th item on input line) | ||
-z option: use Fisher z-transformation | -z option: use Fisher z-transformation | ||
-f option: skip some calculations (fast) | -f option: skip some calculations (fast) | ||
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4 5 0.8432 | 4 5 0.8432 | ||
</pre> | </pre> | ||
Please note that the CCs are rounded to 4 valid digits. This introduces a bit of noise. | Please note that the CCs are rounded to 4 valid digits. This introduces a bit of noise. In addition, the solution in this example is not overdetermined; 5 is just the minimum number of objects that can be represented in 2-dimensional space when given 10 unique correlation coefficients. | ||
<pre> | <pre> | ||
bash-4.2$ cc_analysis -dim 2 cc.dat solution.dat | bash-4.2$ cc_analysis -dim 2 cc.dat solution.dat | ||
CC_ANALYSIS version 29.10.2018 (K. Diederichs). No redistribution please! | CC_ANALYSIS version 29.10.2018 (K. Diederichs). No redistribution please! | ||
Linux turn31.biologie.uni-konstanz.de 4.18.14-1.el7.elrepo.x86_64 #1 SMP Sat Oct 13 10:29:59 EDT 2018 x86_64 x86_64 x86_64 GNU/Linux | Linux turn31.biologie.uni-konstanz.de 4.18.14-1.el7.elrepo.x86_64 #1 SMP Sat Oct 13 10:29:59 EDT 2018 x86_64 x86_64 x86_64 GNU/Linux | ||
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Finished outputting 2-dimensional representative vectors! =) | Finished outputting 2-dimensional representative vectors! =) | ||
</pre> | </pre> | ||
The 5 lines at the bottom give the solution. The coordinates agree with those of the Fortran program within a rms deviation of 0.0055 | The 5 lines at the bottom give the solution. The coordinates agree with those of the Fortran program within a rms deviation of 0.0055 after mirroring across the x axis (inverted solution) and rotating by half a degree. | ||