meanc.m

PCA and PLS aims:to get some insight into the bilinear factor models Principal Component Analysis

function [Zm,mz] = meanc(Z,mz,rev)
% function [Zm,mz] = meanc(Z)
% function [Zm] = meanc(Z,mz)
% function [Zm] = meanc(Z,mz,1)
% 040630 FvdB
% Mean-scales data table Z (new operation or based on know parameters),
% or reverses mean-centering operation.
% 
% in: 
% Z (objects x variables) data block
% mz (1 x variables) column means
% rev (1 x 1) trigger for reversing mean center operation 
%
% out:
% Zm (objects x variables) mean-centered data block, or if 
%    input 'rev' is activated, reverses data block centering
% mz (1 x variables) column means

if (nargin == 0)
    help meanc
    return
end   

[n,m] = size(Z);
if nargin == 1
    if ~sum(sum(isnan(Z)))
        mz = mean(Z);
        Zm = Z-mz(ones(n,1),:);
    else
        Zmv = sparse(isnan(Z));
        zmv = n./(n-sum(Zmv));
        Z(Zmv) = 0;
        mz = mean(Z).*zmv;
        Zm = Z-mz(ones(n,1),:);
        Zm(Zmv) = NaN;
    end
elseif nargin == 2
    Zm = Z-mz(ones(n,1),:);
elseif nargin == 3
    Zm = Z+mz(ones(n,1),:);
end