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Eigenvectors and eigenvalues, sufficient sampling issues
Doing a principal component analysis (PCA) of a trajectory is easy. And is even easier to prepare nice . . . the solute (see [[Calculation of eigenvalues and eigenvectors]] and [[More on eigenvectors and eigenvalues]]). . . . of the fluctuations along the first few eigenvectors. This you do with /xmgr/ (read block data . . . produced by ploting the fluctuations of pairs of eigenvectors. The example that follows shows graphs . . . for the combinations 1-2, 1-3 and 2-3 of eigenvectors. In the case of sufficient sampling, these . . .
5K - last updated 2005-10-17 10:59 UTC by server.cluster.mbg.gr

More on eigenvectors and eigenvalues
Calculation of eigenvalues and eigenvectors is the first step for performing a large number of other . . . the molecule's configurational space. See [[Eigenvectors and eigenvalues, sufficient sampling issues]]. . . . you will see that the top ~10% of eigenvectors accounts for the majority of the observed . . . Successive columns correspond to successive eigenvectors (in decreasing order of corresponding . . . numbers are the amplitudes with which the given eigenvectors contribute to the given frames (ie structures). . . .
6K - last updated 2006-02-22 15:12 UTC by server.cluster.mbg.gr

Tools and methods for analysing MD simulations
Please add other analyses, other methods/programs for performing the same task, etc. * [[Quick view of . . . analysis]] * [[Calculation of eigenvalues and eigenvectors]] * [[More on eigenvectors and eigenvalues]] . . . * [[Eigenvectors and eigenvalues, sufficient sampling issues]] . . . * [[Comparison of sets of eigenvectors]] * [[φ,ψ time series]] * [[χ-value . . .
2K - last updated 2007-02-05 10:15 UTC by server.cluster.mbg.gr

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