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VARIMAX - Rotate EOF's according to varimax algorithm


function [x, r] = varimax( x, normalize, tol, it_max )


 VARIMAX - Rotate EOF's according to varimax algorithm

 This is actually a generic varimax routine and knows nothing special about
 EOFs.  It expects a matrix of "loadings".  Typically (in state space
 rotation), these loadings are the expansion coefficients (aka Principal
 Component Time Series) for the truncated basis of eigenvectors (EOFs), but
 they could also be the EOFs*diag(L)^(1/2) (in the case of rotation in
 sample space).

 Usage: [new_loads, rotmax] = varimax( loadings, normalize, tolerance, it_max )

 where all but the loadings are optional.  rotmax is the rotation matrix used.

 normalize determines whether or not to normalize the rows or columns of
 the loadings before performing the rotation.  If normalize is true, then
 the rows are normalized by there individual lengths.  Otherwise, no
 normalization is performed (default).  After rotation, the matrix is
 renormalized. Normalizing over the rows corresponds to the Kaiser
 normalization often used in factor analysis.

 tolerance defaults to 1e-10 if not given.  it_max specifies the maximum
 number of iterations to do - defaults to 1000.

 After the varimax rotation is performed, the new EOFs (in the case that
 the EC's were rotated - state space) can be found by new_eofs =

 This function is derived from the R function varimax in the mva


     $Id: varimax.m,v 1.4 2002-10-10 00:28:30 dmk Exp $    

 Copyright (C) 2002 David M. Kaplan
 Licence: GPL



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