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Evolving window factor analysis.


[eigs,skl] = ewfa(dat,window,plots,scl)


The inputs are the data matrix dat and the window width window. The output eigs contains the eigenvalues for each window. The windowed eigenvalues vs. sample number is also plotted. Note that the eigenvalues on the ends of the record (less than the half width of the window) are plotted as dashed lines. The output skl is a scale that can be used to plot eigs against.

Optional input plots can be used to suppress plotting when set to 0 {default plots = 1}. Optional input scl is a scale to plot against. It is also used to construct a new skl.


  • dat = input x-block, double array.
  • window = window width.

Optional Inputs

  • plots = [ 0 | {1} ] - 0 suppresses plots.
  • scl = scale to plot eigs against.


  • eigs = singular values for each window.
  • skl = scale to plot eigs against.

See Also

anglemapper, evolvfa, pca, wtfa, mcr, mpca, pcaengine