function  [pval, zscore] = SigClustSM(data,paramstruct) 
% SIGCLUSTSM, statistical SIGnificance of CLUSTers
%   Steve Marron's matlab function
%     Studies whether or not clusters are really there,
%     using 2-means (k = 2) clustering index as a statistic.
%     Gets significance by simulation from a single Gaussian
%     distribution.  Gaussian parameters are estimated from 
%     the data.  Some diagnostics are shown to study performance.
%   Note:  for testing only whether 2 given classes are different,
%     recommended test is DiProPermSM.m 
%         (DIrection-PROjection-PERMutation)
%     The present test focusses on clusters, in the special sense
%     of coming from a single Gaussian
%
% Inputs:
%   data    - d x n matrix of data, each column vector is 
%                    a "d-dim data vector"
%
%   paramstruct - a Matlab structure of input parameters
%                    Use: "help struct" and "help datatypes" to
%                         learn about these.
%                    Create one, using commands of the form:
%
%       paramstruct = struct('field1',values1,...
%                            'field2',values2,...
%                            'field3',values3) ;
%
%                          where any of the following can be used,
%                          these are optional, misspecified values
%                          revert to defaults
%
%    fields            values
%
%    vclass           Vector of 1s and 2s, which indicates given class labels 
%                         (grouping to be tested for significance).
%                     When this is not given, or set to scalar 0, use labels 
%                         generated by 2-means clustering
%                     Must be a row vector, of length n, 
%                         otherwise execution is terminated
%
%    sigbackg         Background Standard Deviation, given value.
%                     When this is not given, or set to scalar 0, 
%                     estimate from Data.
%
%    iCovEst          Covariance Estimation Type
%                     1 - Use Hanwen Huang's Soft Thresholded, Constrained MLE
%                           (default, and recommended)
%                           (from Huang, Liu, Yuan, Marron 2014 JCGS paper)
%                     2 - Use Sample Covariance Estimate
%                           (recommended when diagnostics fail)
%                     3 - Use Original Background Noise Thresholded Estimate
%                           (from Liu, et al, 2008 JASA paper)
%                           (In Yuan's terminology, this is "Hard Thresholding")
%                           (this is anti-conservative for a few 
%                                very large eigenvalues)
%                     4 - Use Ming Yuan's Soft Thresholded, Constrained MLE
%                           (from Ming Yuan's notes, 2007)
%                           (this is anti-conservative when the first 
%                                eigenvalue is relatively small)
%
%    ipvaltype        Type of p-value to compute
%                     1 - Only p-value (empirical quantile)
%                     2 - Only Z-score (useful for comparisons, when
%                                            p-values are all 0)
%                     3 - (default) Both p-value (empirical quantile)
%                                and Z-score
%                           (note:  uncomputed p-values are returned as empty,
%                                     and not shown in output plots)
%
%    twoMtype         Type of 2-means clusterings to compute
%                     1 - use simple random restarts
%                           (i.e. make calls to SigClust2meanRepSM.m)
%                     2 (default) - use PCA starts
%                           (i.e. make calls to SigClust2meanFastSM.m)
%
%    twoMsteps        Number of steps to use in 2 means clustering
%                         computations
%                     (default = 1, chosen to optimize speed)
%
%    nsim             Number of simulated Gaussian samples to use
%                     for main p-value computation
%                     (default = 1000)
%
%    InitRandstate    State of uniform random number generator,
%                         for Initial Calculation of Data Cluster Index
%                     When empty, or not specified, just use current seed  
%                         (has no effect, unless twoMtype = 1 & vclass = 0)
%
%    InitRandnstate   State of normal random number generator,
%                         for Initial Calculation of Data Cluster Index
%                     When empty, or not specified, just use current seed  
%                         (has no effect, unless twoMtype = 1 & vclass = 0)
%
%    SimRandstate     State of uniform random number generator,
%                         for Main Simulation
%                     When empty, or not specified, just use current seed  
%                         (has no effect, unless twoMtype = 1)
%
%    SimRandnstate    State of normal random number generator,
%                         for Main Simulation
%                     When empty, or not specified, just use current seed  
%                         (has no effect, unless twoMtype = 1)
%
%    datastr          String Descriptive of data set,
%                       mostly for use in graphical output,
%                       when these are requested.
%                     Often may want to end this with "data"
%                         (default is [])
%
%    iBGSDdiagplot    0  do not make BackGround Standard Deviation
%                           DIAGnostic PLOTs
%                     1  (default) make BackGround Standard Deviation
%                           DIAGnostic PLOTs:
%                             i.   Overlay and density plot of pixel dist'n,
%                                      with robust Gaussian fit
%                             ii.  QQ plot assessing quality of robust fit
%                                      of Gaussian Distribution
%                           This has no effect, unless sigbackg == 0
%
%    BGSDsavestr      string controlling saving of output for BackGround 
%                         Standard Deviation DIAGnostic PLOTs,
%                         either a full path, or a file prefix to
%                         save in matlab's current directory.
%                         Probably makes sense to end with 
%                         something like:   'AllPixel'
%                         Will add:
%                             'KDE' for plot i.
%                             'QQ' for plot ii.
%                         Will also add .ps (so don't do this again!), 
%                         and save as color postscript files.
%                         Unspecified (or empty):
%                             Results only appear on screen
%                         Only has effect when iBGSDdiagplot = 1
%
%    iCovEdiagplot    0  do not make COVariance Estimation
%                           DIAGnostic PLOT
%                     1  (default) make COVariance Estimation
%                           DIAGnostic PLOT
%
%    CovEsavestr      string controlling saving of output for 
%                         COVariance Estimation DIAGnostic PLOTs,
%                         either a full path, or a file prefix to
%                         save in matlab's current directory.
%                         Probably makes sense to end with 
%                         something like:         'EstEigVal'
%                         Will also add .ps (so don't do this again!), 
%                         and save as color postscript files.
%                         Unspecified (or empty):
%                             Results only appear on screen
%                         Only has effect when iCovEdiagplot = 1
%
%    ipValplot        0  do not make p Value plot
%                     1  (default) make p Value plot
%
%    pValsavestr      string controlling saving of output for 
%                         p Value plot,
%                         either a full path, or a file prefix to
%                         save in matlab's current directory.
%                         Probably makes sense to end with 
%                         something like:         'pVal'
%                         Will also add .ps (so don't do this again!), 
%                         and save as color postscript files.
%                         Unspecified (or empty):
%                             Results only appear on screen
%                         Only has effect when ipValplot = 1
%
%    legendcellstr    For p Value Plot:
%                     cell array of strings for legend (nl of them),
%                         useful for (colored) classes, create this using
%                         cellstr, or {{string1 string2 ...}}
%                     Note:  These strange double brackets seems to be needed
%                                for correct pass to subroutine
%                                It may change in later versions of Matlab
%                     CAUTION:  If are updating this field, using a command like:
%                         paramstruct = setfield(paramstruct,'legendcellstr',...
%                     Then should only use single braces in the definition of
%                     legendecellstr, i. e. {string1 string2 ...}
%                     Suggested uses:
%                       For vclass = 0:
%                         legendcellstr = {{'Testing Best 2-means Split'}}
%                       For given vclass:
%                         legendcellstr = {{'Class 1' 'vs.' 'Class 2'}}
%                             then use mlegendcolor appropriately,
%                             e.g.  mlegendcolor = [[1 0 0]; [0 0 0]; [0 0 1]]
%                     Only has effect when ipValplot = 1
%
%    mlegendcolor     For p Value Plot:
%                     nl x 3 color matrix, corresponding to cell legends above
%                         (not needed when legendcellstr not specified)
%                         (defaults to black when not specified)
%                     Only has effect when ipValplot = 1
%
%    iscreenwrite     0  (default)  no screen writes
%                             except warning messages
%                     1  write to screen to show major steps
%                     2  to show most steps, but only one write at 
%                             each step of main simulation loop
%                     3  to show all steps, not recommended since it
%                             makes multiple writes for each step of
%                             main simulation loop
%
%
% Outputs:
%
%    pval             Simulated SigClust P-value,
%                     Based on Empirical Quantiles, 
%                     Computed by cquantSM.m
%
%    zscore           Z-score summary (in permutation population),
%                     useful for comparisons, when pval = 0
%
%    Graphics in different Figure windows
%    When ___savestr exists,
%        Color Postscript files saved in '___savestr'.ps
%
% Assumes path can find personal functions:
%    SigClust2meanRepSM.m
%    SigClust2meanFastSM.m
%    SigClustCovEstHH.m
%    SigClustCovEstSM.m
%    SigClustLabelPlotSM.m
%    SigClust2meanPerfSM.m
%    vec2matSM.m
%    axisSM.m
%    scatplotSM.m
%    bwsjpiSM.m
%    kdeSM.m
%    lbinrSM.m
%    vec2matSM.m
%    pcaSM.m
%    projplot1SM.m
%    projplot2SM.m
%    bwrfphSM.m
%    bwosSM.m
%    rootfSM.m
%    bwrotSM.m
%    bwsnrSM.m
%    iqrSM.m
%    cquantSM.m
%    nmfSM.m


%    Copyright (c) J. S. Marron 2007,2008,2009,2014



%  First set all parameters to defaults
%
vclass = 0 ;
sigbackg = 0 ;
iCovEst = 1 ;
ipvaltype = 3 ;
twoMtype = 2 ;
twoMsteps = 1 ;
nsim = 1000 ;
InitRandstate = [] ;
InitRandnstate = [] ;
SimRandstate = [] ;
SimRandnstate = [] ;
datastr = [] ;
iBGSDdiagplot = 1 ;
BGSDsavestr = [] ;
iCovEdiagplot = 1 ;
CovEsavestr = [] ;
ipValplot = 1 ;
pValsavestr = [] ;
legendcellstr = {} ;
mlegendcolor = [] ;
iscreenwrite = 0 ;



%  Now update parameters as specified,
%  by parameter structure (if it is used)
%
if nargin > 1 ;   %  then paramstruct is an argument

  if isfield(paramstruct,'vclass') ;    %  then change to input value
    vclass = getfield(paramstruct,'vclass') ; 
  end ;

  if isfield(paramstruct,'sigbackg') ;    %  then change to input value
    sigbackg = getfield(paramstruct,'sigbackg') ; 
  end ;

  if isfield(paramstruct,'iCovEst') ;    %  then change to input value
    iCovEst = getfield(paramstruct,'iCovEst') ; 
  end ;

  if isfield(paramstruct,'ipvaltype') ;    %  then change to input value
    ipvaltype = getfield(paramstruct,'ipvaltype') ; 
  end ;

  if isfield(paramstruct,'twoMtype') ;    %  then change to input value
    twoMtype = getfield(paramstruct,'twoMtype') ; 
  end ;

  if isfield(paramstruct,'twoMsteps') ;    %  then change to input value
    twoMsteps = getfield(paramstruct,'twoMsteps') ; 
  end ;

  if isfield(paramstruct,'nsim') ;    %  then change to input value
    nsim = getfield(paramstruct,'nsim') ; 
  end ;

  if isfield(paramstruct,'InitRandstate') ;    %  then change to input value
    InitRandstate = getfield(paramstruct,'InitRandstate') ; 
  end ;

  if isfield(paramstruct,'InitRandnstate') ;    %  then change to input value
    InitRandnstate = getfield(paramstruct,'InitRandnstate') ; 
  end ;

  if isfield(paramstruct,'SimRandstate') ;    %  then change to input value
    SimRandstate = getfield(paramstruct,'SimRandstate') ; 
  end ;

  if isfield(paramstruct,'SimRandnstate') ;    %  then change to input value
    SimRandnstate = getfield(paramstruct,'SimRandnstate') ; 
  end ;

  if isfield(paramstruct,'datastr') ;    %  then change to input value
    datastr = getfield(paramstruct,'datastr') ; 
  end ;

  if isfield(paramstruct,'iBGSDdiagplot') ;    %  then change to input value
    iBGSDdiagplot = getfield(paramstruct,'iBGSDdiagplot') ; 
  end ;

  if isfield(paramstruct,'BGSDsavestr') ;    %  then change to input value
    BGSDsavestr = getfield(paramstruct,'BGSDsavestr') ; 
    if ~(ischar(BGSDsavestr) | isempty(BGSDsavestr)) ;    %  then invalid input, so give warning
      disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
      disp('!!!   Warning from SigClustSM.m:   !!!') ;
      disp('!!!   Invalid BGSDsavestr,         !!!') ;
      disp('!!!   using default of no save     !!!') ;
      disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
      BGSDsavestr = [] ;
    end ;
  end ;

  if isfield(paramstruct,'iCovEdiagplot') ;    %  then change to input value
    iCovEdiagplot = getfield(paramstruct,'iCovEdiagplot') ; 
  end ;

  if isfield(paramstruct,'CovEsavestr') ;    %  then change to input value
    CovEsavestr = getfield(paramstruct,'CovEsavestr') ; 
    if ~(ischar(CovEsavestr) | isempty(CovEsavestr)) ;    %  then invalid input, so give warning
      disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
      disp('!!!   Warning from SigClustSM.m:   !!!') ;
      disp('!!!   Invalid CovEsavestr,         !!!') ;
      disp('!!!   using default of no save     !!!') ;
      disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
      CovEsavestr = [] ;
    end ;
  end ;

  if isfield(paramstruct,'ipValplot') ;    %  then change to input value
    ipValplot = getfield(paramstruct,'ipValplot') ; 
  end ;

  if isfield(paramstruct,'pValsavestr') ;    %  then change to input value
    pValsavestr = getfield(paramstruct,'pValsavestr') ; 
    if ~(ischar(pValsavestr) | isempty(pValsavestr)) ;    %  then invalid input, so give warning
      disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
      disp('!!!   Warning from SigClustSM.m:   !!!') ;
      disp('!!!   Invalid pValsavestr,         !!!') ;
      disp('!!!   using default of no save     !!!') ;
      disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
      pValsavestr = [] ;
    end ;
  end ;

  if isfield(paramstruct,'legendcellstr') ;    %  then change to input value
    legendcellstr = getfield(paramstruct,'legendcellstr') ; 
  end ;

  if isfield(paramstruct,'mlegendcolor') ;    %  then change to input value
    mlegendcolor = getfield(paramstruct,'mlegendcolor') ; 
  end ;

  if isfield(paramstruct,'iscreenwrite') ;    %  then change to input value
    iscreenwrite = getfield(paramstruct,'iscreenwrite') ; 
  end ;

end ;    %  of resetting of input parameters




%  set preliminary stuff
%
d = size(data,1) ;
         %  dimension of each data curve
n = size(data,2) ;
         %  number of data curves
pval = [] ;
zscore = [] ;
CurFigNum = 0 ;
if iscreenwrite == 2 ;
  iscreenwritemost = 1 ;
  iscreenwriteall = 0 ;
      %  for use as arguments in other calls
elseif iscreenwrite == 3 ;
  iscreenwritemost = 1 ;
  iscreenwriteall = 1 ;
      %  for use as arguments in other calls
else ;
  iscreenwritemost = 0 ;
  iscreenwriteall = 0 ;
      %  for use as argument in other calls
end ;

if isempty(vclass) ;
  disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
  disp('!!!   Warning from SigClustSM.m:     !!!') ;
  disp('!!!   vclass is empty                !!!') ;
  disp('!!!   Resetting to vclass = 0        !!!') ;
  disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
  vclass = 0 ;
end ;
if vclass ~= 0 ;
  if  (size(vclass,1) ~= 1)  |   (size(vclass,2) ~= n)  ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    disp('!!!   Error from SigClustSM.m:          !!!') ;
    disp('!!!   vclass needs to be a row vector   !!!') ;
    disp(['!!!       of length ' num2str(n)]) ;
    disp('!!!   Terminating Execution             !!!') ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    return ;
  end ;
  flag1 =  (vclass == 1)  ;
  flag2 =  (vclass == 2)  ;
  if (sum(flag1) + sum(flag2)) ~= n ; 
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    disp('!!!   Error from SigClustSM.m:                !!!') ;
    disp('!!!   vclass needs to contain all 1s and 2s   !!!') ;
    disp('!!!   Terminating Execution                   !!!') ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    return ;
  end ;
end ;

if  ~isempty(mlegendcolor) ;
  if ~(size(legendcellstr,2) == size(mlegendcolor,1)) ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    disp('!!!   Warning from SigClustSM.m:        !!!') ;
    disp('!!!   legendcellstr &  mlegendcolor     !!!') ;
    disp('!!!   must have the same number         !!!') ;
    disp('!!!   of entries                        !!!') ;
    disp('!!!   Note: this could be caused by     !!!') ;
    disp('!!!   using "setfield(paramstruct..."   !!!') ;
    disp('!!!   with "lengedcellstr" defined      !!!') ;
    disp('!!!   by double braces                  !!!') ;
    disp('!!!   of entries                        !!!') ;
    disp('!!!   Resetting to no legend            !!!') ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    legendcellstr = [] ;
  end ;
end ;

if  ~isempty(mlegendcolor) ;
  if ~(size(mlegendcolor,2) == 3) ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    disp('!!!   Warning from SigClustSM.m:    !!!') ;
    disp('!!!   mlegendcolor                  !!!') ;
    disp('!!!   must have 3 columns           !!!') ;
    disp('!!!   Resetting to no legend        !!!') ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    legendcellstr = [] ;
  end ;
end ;



%  Compute Initial Cluster Index for Data
%
if vclass == 0 ;    %  No initial clustering given, so compute from data

  if twoMtype == 1 ;    %  Use Random Restarts for computation

    paramstruct = struct('nrep',twoMsteps,  ...
                         'randstate',InitRandstate, ...
                         'randnstate',InitRandnstate, ...
                         'iscreenwrite',iscreenwritemost) ;
    [temp, vindex] = SigClust2meanRepSM(data,paramstruct) ;
        %  Ignore usual output of BestClass (Cluster Labelling)
    ClustIndData = min(vindex) ;
        %  Since SigClust2meanRepSM returns full vector of Clust Indices

  elseif twoMtype == 2 ;    %  Use PCA starts for computation

    paramstruct = struct('maxstep',twoMsteps,  ...
                         'ioutplot',0, ...
                         'iscreenwrite',iscreenwritemost) ;
    [temp, ClustIndData]= SigClust2meanFastSM(data,paramstruct) ;
        %  Ignore usual output of BestClass (Cluster Labelling)

  else ;    %  Mispecified Computation Type

    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    disp('!!!   Error from SigClustSM.m:    !!!') ;
    disp('!!!   Invalid value of twoMtype   !!!') ;
    disp(['!!!   twoMtype = ' num2str(twoMtype)]) ;
    disp('!!!   Terminating Execution       !!!') ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    return ;

  end ;

else ;    %  Were given an initial clustering, so compute ClustInd for that

  ClustIndData = ClustIndSM(data,(vclass == 1),(vclass == 2)) ;

end ;



%  Estimate covariance structure of data
%
%  Start with sample eignevalues
%
paramstruct = struct('iscreenwrite',iscreenwritemost, ...
                     'viout',[1]) ;
outstruct = pcaSM(data,paramstruct) ;
veigval = getfield(outstruct,'veigval') ;
nev = length(veigval) ;
veigval = [veigval; zeros(d-nev,1)] ;
     %  Pad out to have length d

varflag = 0 ;
    %  warning flag about possible bad background estimation


if iCovEst ~= 2 ;    %  then need to consider thresholding

  npix = d * n ;
  vdata = reshape(data,npix,1) ;
      %  all pixel data
  asd = std(vdata) ;
  avar = asd^2 ;


  if sigbackg == 0 ;    %  Need to estimate background standard deviation


    amean = mean(vdata) ;
    amedian = median(vdata) ;
    amad = madSM(vdata) ;
        %  MAD, but on sd scale


    if iBGSDdiagplot ~= 0 ;    %  Then make BackGround Standard Deviation
                               %        DIAGnostic PLOTs

      CurFigNum = CurFigNum + 1 ;
      figure(CurFigNum) ;
      clf ;
      maxnol = 5000 ;
      paramstruct = struct('ndatovlay',maxnol, ...
                           'datovlaymax',0.65, ...
                           'datovlaymin',0.15, ...
                           'iscreenwrite',iscreenwritemost) ;
      kdeSM(vdata,paramstruct) ;
        if isempty(datastr) ;
          titstr = 'Distribution of All Pixel values combined' ;
        else ;
          titstr = ['Distribution of All Pixel values combined, ' datastr] ;
        end ;
        title(titstr,'FontSize',15) ;
        vax = axis ;
        hold on ;
            xgrid = linspace(vax(1),vax(2),401)' ;
            normden = nmfSM(xgrid,amedian,amad^2,1) ;
          plot(xgrid,normden,'r--') ;
          if length(vdata) < maxnol ;
            olstr = ['Overlay of '...
                     num2str(length(vdata)) ' data points'] ;
          else ;
            olstr = ['Overlay of ' num2str(maxnol) ' of '...
                     num2str(length(vdata)) ' data points'] ;
          end ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.9 * (vax(4) - vax(3)), ...
               olstr,'FontSize',15) ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.8 * (vax(4) - vax(3)), ...
               ['Mean = ' num2str(amean,3) ...
                ',     median = ' num2str(amedian,3)], ...
               'FontSize',15) ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.7 * (vax(4) - vax(3)), ...
               ['s.d. = ' num2str(asd,3) ...
                ',     MAD = ' num2str(amad,3)], ...
               'FontSize',15) ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.6 * (vax(4) - vax(3)), ...
               ['Gaussian(' num2str(amedian,3) ...
                ',' num2str(amad,3) ') density'], ...
               'FontSize',15, 'Color','r') ;
          if amad > asd ;
            text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
                 vax(3) + 0.55 * (vax(4) - vax(3)), ...
                 ['Warning: MAD > s.d., SigClust can be Anti-Conservative'], ...
                 'FontSize',24, 'Color','m') ;
          end ;
        hold off ;

        if ~isempty(BGSDsavestr) ;
          orient landscape ;
            savestr = [BGSDsavestr 'KDE.ps'] ;
          print('-dpsc2',savestr) ;
        end ;


      CurFigNum = CurFigNum + 1 ;
      figure(CurFigNum) ;
      clf ;
      savestr = [BGSDsavestr 'QQ'] ;

      if isempty(datastr) ;
        titstr = 'Robust Fit Gaussian Q-Q, All Pixel values' ;
      else ;
        titstr = ['Robust Fit Gaussian Q-Q, All Pixel values, ' datastr] ;
      end ;

      paramstruct = struct('idist',1, ...
                           'mu',amedian, ...
                           'sigma',amad, ...
                           'nsim',0, ...
                           'nsimplotval',900, ...
                           'savestr',savestr, ...
                           'titlestr',titstr, ...
                           'titlefontsize',15, ...
                           'labelfontsize',15, ...
                           'parfontsize',15, ...
                           'ishowpar',1, ...
                           'vshowq',[0.25; 0.5; 0.75], ...
                           'iscreenwrite',iscreenwritemost) ;
%      qqLM(vdata,paramstruct) ;

    end ;    %  of iBGSDdiagplot if-block

    isigbackg = amad ;

  else ;    %  Use input value of background standard deviation

    isigbackg = sigbackg ;

  end ;    %  of sigbackg if-block


  simbackvar = isigbackg^2 ;
      %  Background variance to use in simulations


  %  Check whether background estimate is sensible,
  %  in sense of being smaller than sample variance
  if simbackvar > avar ;    %  then have surprising background var est,
                            %      so give warning, and recommendations
    varflag = 1 ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    disp('!!!   Warning from SigClustSM.m:                    !!!') ;
    disp('!!!   Background Variance Estimate is Suspect       !!!') ;
    disp('!!!   Because (MAD estimated sig2BG) >              !!!') ;
    disp('!!!                  > (full matrix sample sd),     !!!') ;
    disp(['!!!   by factor of ' num2str(simbackvar / avar)]) ;
    disp('!!!   This could make SigClust Anti-Conservative    !!!') ;
    disp('!!!   Recommend Careful look at Diagnostic plots,   !!!') ;
    disp('!!!       using:   iBGSDdiagplot = 1                !!!') ;
    disp('!!!   Consider re-running SigClust test,            !!!') ;
    disp('!!!       using:   iCovEst = 2                      !!!') ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
  end ;

  %  Compute eigenvalues to use in Gaussian Simulation
  %
  if iCovEst == 3 ;    %  Use Original Background Noise Thresholded Estimate
                       %     (from Liu, et al, JASA paper)
                       %     (In Yuan's terminology, this is "Hard Thresholding")

    vsimeigval = max(veigval,simbackvar) ;
        %  vector of eigenvalues for simulation

  elseif iCovEst == 4 ;    %  Use Ming Yuan's Soft Thresholded, Constrained MLE

    vsimeigval = SigClustCovEstSM(veigval,simbackvar) ;
        %  vector of eigenvalues for simulation

  else ;    %  Use Hanwen Huang's Soft Thresholded, Constrained MLE

    vsimeigval = SigClustCovEstHH(veigval,simbackvar) ;
        %  vector of eigenvalues for simulation

  end ;


else ;   %  no adjustment to eigenvlues, just use empiricals

  vsimeigval = veigval ;
      %  vector of eigenvalues for simulation
      %  based on raw covariance type model

end ;    %  of (iCovEst ~= 2) if-block



if iCovEdiagplot == 1 ;    %  Then make COVariance Estimation DIAGnostic PLOT

  ncut = 100 ;
  CurFigNum = CurFigNum + 1 ;
  figure(CurFigNum) ;
  clf ;

  veigvalpos = veigval(veigval > 10^(-12)) ;
  dpos = length(veigvalpos) ;

  subplot(2,2,1) ;
    plot((1:d)',veigval,'ko-') ;
      if isempty(datastr) ;
        titstr = 'Eigenvalues' ;
      else ;
        titstr = ['Eigenvalues, ' datastr] ;
      end ;
      title(titstr,'FontSize',12) ;
      xlabel('Component #','FontSize',12) ;
      ylabel('Eigenvalue','FontSize',12) ;
      if iCovEst == 2 ;
        vax = axisSM(veigval) ;
      else ;
        vax = axisSM([veigval; simbackvar]) ;
      end ;
      vax = [0 (d+1) vax(1) vax(2)] ;
      axis(vax) ;
      hold on ;
        plot([ncut + 0.5; ncut + 0.5],[vax(3); vax(4)],'g-') ;
        plot((1:d)',vsimeigval,'r--','LineWidth',2) ;
        if iCovEst ~= 2 ;
          plot([0; d + 1],[simbackvar; simbackvar],'m-') ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.9 * (vax(4) - vax(3)), ...
               ['Background variance = ' num2str(simbackvar,3)], ...
               'FontSize',12,'Color','m') ;
        end ;
        text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
             vax(3) + 0.8 * (vax(4) - vax(3)), ...
             'Eigenvalues for Simulation', ...
             'FontSize',12,'Color','r') ;
        if varflag == 1 ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.65 * (vax(4) - vax(3)), ...
               ['Warning: MAD > s.d.,'], ...
               'FontSize',18,'Color','m') ;
        end ;
      hold off ;
  subplot(2,2,2) ;
    plot((1:dpos)',log10(veigvalpos),'ko-') ;
      title('log_{10} Eigenvalues','FontSize',12) ;
      xlabel('Component #','FontSize',12) ;
      ylabel('log_{10}(Eigenvalue)','FontSize',12) ;
      if iCovEst == 2 ;
        vax = axisSM(log10(veigvalpos)) ;
      else ;
        vax = axisSM(log10([veigvalpos; simbackvar])) ;
      end ;
      vax = [0 (d+1) vax(1) vax(2)] ;
      axis(vax) ;
      hold on ;
        plot([ncut + 0.5; ncut + 0.5],[vax(3); vax(4)],'g-') ;
        plot((1:d)',log10(vsimeigval),'r--','LineWidth',2) ;
        if iCovEst ~= 2 ;
          plot([0; d + 1],log10([simbackvar; simbackvar]),'m-') ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.9 * (vax(4) - vax(3)), ...
               ['log_{10} Background variance = ' num2str(log10(simbackvar),3)], ...
               'FontSize',12,'Color','m') ;
        end ;
        text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
             vax(3) + 0.8 * (vax(4) - vax(3)), ...
             'Eigenvalues for Simulation', ...
             'FontSize',12,'Color','r') ;
        if varflag == 1 ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.65 * (vax(4) - vax(3)), ...
               ['SigClust may be Anti-Conservative'], ...
               'FontSize',18,'Color','m') ;
        end ;
      hold off ;
  if length(veigval) >= ncut ;
    subplot(2,2,3) ;
      plot((1:ncut)',veigval(1:ncut),'ko-') ;
        title('Zoomed in version of above','FontSize',12) ;
        xlabel('Component #','FontSize',12) ;
        ylabel('Eigenvalue','FontSize',12) ;
        vax = axisSM(veigval(1:ncut)) ;
        vax = [0 (ncut+1) vax(1) vax(2)] ;
        axis(vax) ;
        hold on ;
          plot((1:ncut)',vsimeigval(1:ncut),'r--','LineWidth',2) ;
          if iCovEst ~= 2 ;
            plot([0; ncut + 1],[simbackvar; simbackvar],'m-') ;
            text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
                 vax(3) + 0.9 * (vax(4) - vax(3)), ...
                 ['Background variance = ' num2str(simbackvar,3)], ...
                 'FontSize',12,'Color','m') ;
          end ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.8 * (vax(4) - vax(3)), ...
               'Eigenvalues for Simulation', ...
               'FontSize',12,'Color','r') ;
        hold off ;
    subplot(2,2,4) ;
      plot((1:min(dpos,ncut))',log10(veigvalpos(1:min(dpos,ncut))),'ko-') ;
        title('Zoomed in version of above','FontSize',12) ;
        xlabel('Component #','FontSize',12) ;
        ylabel('log_{10}(Eigenvalue)','FontSize',12) ;
        if iCovEst == 2 ;
          vax = axisSM(log10(veigvalpos(1:min(dpos,ncut)))) ;
        else ;
          vax = axisSM(log10([veigvalpos(1:min(dpos,ncut)); simbackvar])) ;
        end ;
        vax = [0 (ncut+1) vax(1) vax(2)] ;
        axis(vax) ;
        hold on ;
          plot((1:ncut)',log10(vsimeigval(1:ncut)),'r--','LineWidth',2) ;
          if iCovEst ~= 2 ;
            plot([0; d + 1],log10([simbackvar; simbackvar]),'m-') ;
            text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
                 vax(3) + 0.9 * (vax(4) - vax(3)), ...
                 ['log_{10} Background variance = ' num2str(log10(simbackvar),3)], ...
                 'FontSize',12,'Color','m') ;
            end ;
          text(vax(1) + 0.1 * (vax(2) - vax(1)), ...
               vax(3) + 0.8 * (vax(4) - vax(3)), ...
               'Eigenvalues for Simulation', ...
               'FontSize',12,'Color','r') ;
        hold off ;
  end ;

  if ~isempty(CovEsavestr) ;
    orient landscape ;
      savestr = [CovEsavestr '.ps'] ;
    print('-dpsc2',savestr) ;
  end ;

end ;    %  of iCovEdiagplot if-block

if  (iscreenwrite == 1)  |  (iscreenwrite == 2)  |  (iscreenwrite == 3)  ;
  if isempty(datastr) ;
    dispstr = 'SigClustSM:  Finished Estimation of Parameters for Gaussian simulation' ;
  else ;
    dispstr = ['SigClustSM:  Finished Estimation of Parameters for Gaussian simulation, for ' datastr] ;
  end ;
  disp(dispstr) ;
end ;



%  Main Simulation Loop
%
if ~isempty(SimRandstate) ;    %  Then set random number generation seed
  rand('state',SimRandstate) ;
end ;
if ~isempty(SimRandnstate) ;    %  Then set random number generation seed
  randn('state',SimRandnstate) ;
end ;

vscale = sqrt(vsimeigval) ;

vSimIndex = [] ;
for isim = 1:nsim ;
  if  iscreenwrite == 2  | (iscreenwrite == 3) ;
    disp(['      SigClustSM:  Working on sim ' num2str(isim) ' of ' num2str(nsim)]) ;
  end ;

  mdatsim = randn(d,n) ;
  mdatsim = mdatsim .* vec2matSM(vscale,n) ;

  if twoMtype == 1 ;    %  Use Random Restarts for computation

    paramstruct = struct('nrep',twoMsteps,  ...
                         'iscreenwrite',iscreenwriteall) ;
    [temp, vindex] = SigClust2meanRepSM(mdatsim,paramstruct) ;
        %  Ignore usual output of BestClass (Cluster Labelling)
    ClustIndSim = min(vindex) ;
        %  Since SigClust2meanRepSM returns full vector of Clust Indices

  elseif twoMtype == 2 ;    %  Use PCA starts for computation

    paramstruct = struct('maxstep',twoMsteps,  ...
                         'ioutplot',0, ...
                         'iscreenwrite',iscreenwriteall) ;
    [temp, ClustIndSim]= SigClust2meanFastSM(mdatsim,paramstruct) ;
        %  Ignore usual output of BestClass (Cluster Labelling)

  else ;    %  Mispecified Computation Type

    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    disp('!!!   Error from SigClustSM.m:    !!!') ;
    disp('!!!   Invalid value of twoMtype   !!!') ;
    disp(['!!!   twoMtype = ' num2str(twoMtype)]) ;
    disp('!!!   Terminating Execution       !!!') ;
    disp('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') ;
    return ;

  end ;

  vSimIndex = [vSimIndex; ClustIndSim] ;
end ;

if  (iscreenwrite == 1)  |  (iscreenwrite == 2)  |  (iscreenwrite == 3)  ;
  if isempty(datastr) ;
    dispstr = 'SigClustSM:  Finished main simulation' ;
  else ;
    dispstr = ['SigClustSM:  Finished main simulation, for ' datastr] ;
  end ;
  disp(dispstr) ;
end ;




%  Output Final results
%
if ipvaltype ~= 2 ;    %  then compute Empirical Quantile based p-value
  pval = cprobSM(vSimIndex,ClustIndData) ;
end ;
if ipvaltype ~= 1 ;    %  then compute Z score
  simmean = mean(vSimIndex) ;
  simsd = std(vSimIndex) ;
  zscore = (ClustIndData - simmean) / simsd ;
end ;


if ipValplot == 1 ;    %  Then make p Value plot

  CurFigNum = CurFigNum + 1 ;
  figure(CurFigNum) ;
  clf ;

  vax = axisSM([vSimIndex; ClustIndData]) ;
  if isempty(datastr) ;
    kdetitstr = [num2str(nsim) ' Gaussian Simulated Cluster Indices'] ;
  else ;
    kdetitstr = [num2str(nsim) ' Gaussian Simulated Cluster Indices, ' datastr] ;
  end ;

  kdeparamstruct = struct('vxgrid',vax, ...
                          'linecolor','k', ...
                          'dolcolor','k', ...
                          'ibigdot',1, ...
                          'titlestr',kdetitstr, ...
                          'titlefontsize',12, ...
                          'xlabelstr','Cluster Index', ...
                          'ylabelstr','density', ...
                          'labelfontsize',12, ...
                          'datovlaymin',0.4, ...
                          'datovlaymax',0.6, ...
                          'iscreenwrite',iscreenwritemost) ;
  kdeSM(vSimIndex,kdeparamstruct) ;
  vax = axis ;
  hold on ;

    plot([ClustIndData; ClustIndData],[vax(3); vax(4)],'Color',[0 0.6 0]) ;
    text(vax(1) + 0.5 * (vax(2) - vax(1)), ...
         vax(3) + 0.9 * (vax(4) - vax(3)), ...
         ['Data Cluster Index = ' num2str(ClustIndData)],'Color',[0 0.6 0]) ;
    if ipvaltype ~= 2 ;    %  then write Empirical Quantile based p-value
      text(vax(1) + 0.5 * (vax(2) - vax(1)), ...
           vax(3) + 0.8 * (vax(4) - vax(3)), ...
           ['p-val (Empirical) = ' num2str(pval)],'Color',[0 0.6 0]) ;
    end;
    if ipvaltype ~= 1 ;    %  then write Gaussian based p-value
      text(vax(1) + 0.5 * (vax(2) - vax(1)), ...
           vax(3) + 0.7 * (vax(4) - vax(3)), ...
           ['Z Score = ' num2str(zscore)],'Color',[0 0.6 0]) ;
      xgrid = linspace(vax(1),vax(2),401)' ;
      plot(xgrid,nmfSM(xgrid,simmean,simsd^2,1),'k') ;
    end ;
    if varflag == 1 ;
      text(vax(1) + 0.5 * (vax(2) - vax(1)), ...
           vax(3) + 0.55 * (vax(4) - vax(3)), ...
           ['Warning: MAD > s.d.,'], ...
           'FontSize',15,'Color','m') ;
      text(vax(1) + 0.5 * (vax(2) - vax(1)), ...
           vax(3) + 0.45 * (vax(4) - vax(3)), ...
           ['by factor of ' num2str(simbackvar / avar) ','], ...
           'FontSize',15,'Color','m') ;
      text(vax(1) + 0.5 * (vax(2) - vax(1)), ...
           vax(3) + 0.35 * (vax(4) - vax(3)), ...
           ['SigClust may be Anti-Conservative'], ...
           'FontSize',15,'Color','m') ;
    end ;

    if ~isempty(legendcellstr) ;    %  then add legend
      nlegend = length(legendcellstr) ;
      if isempty(mlegendcolor) ;
        mlegendcolor = vec2matSM(zeros(1,3),nlegend) ;
            %  all black when unspecified
      end ;
      tx = vax(1) + 0.1 * (vax(2) - vax(1)) ;
      for ilegend = 1:nlegend ;
        ty = 0 + ((nlegend - ilegend + 1) / ...
                             (nlegend + 1)) * (vax(4) - 0) ;
        text(tx,ty,legendcellstr(ilegend),  ...
                  'Color',mlegendcolor(ilegend,:)) ;
      end ;
    end ;

  hold off ;

  if ~isempty(pValsavestr) ;
    orient landscape ;
      savestr = [pValsavestr '.ps'] ;
    print('-dpsc2',savestr) ;
  end ;

end ;    %  of ipValplot if-block


if  (iscreenwrite == 1)  |  (iscreenwrite == 2)  |  (iscreenwrite == 3)  ;
  if isempty(datastr) ;
    if ipvaltype == 1 ;    %  then write Quantile based p-value
      dispstr = ['SigClustSM:  Finished with p-value = ' num2str(pval)] ;
    elseif ipvaltype == 2 ;    %  then write Gaussian based p-value
      dispstr = ['SigClustSM:  Finished with Z-Score = ' num2str(zscore)] ;
    else ;    %  then write both p-values
      dispstr = ['SigClustSM:  Finished with p-value = ' num2str(pval) ...
                                       '   & Z-Score = ' num2str(zscore)] ;
    end ;
  else ;
    if ipvaltype == 1 ;    %  then write Quantile based p-value
      dispstr = ['SigClustSM:  Finished, for ' datastr ...
                                      ', with p-value = ' num2str(pval)] ;
    elseif ipvaltype == 2 ;    %  then write Gaussian based p-value
      dispstr = ['SigClustSM:  Finished, for ' datastr ...
                                      ', with Z-Score = ' num2str(zscore)] ;
    else ;    %  then write both p-values
      dispstr = ['SigClustSM:  Finished, for ' datastr ...
                                      ', with p-value = ' num2str(pval) ...
                                       '   & Z-Score = ' num2str(zscore)] ;
    end ;
  end ;
  disp(dispstr) ;
end ;


