SurfStatQ

Q-values for False Discovey Rate of resels.

 Usage: qval = SurfStatQ( slm [, mask] );

 slm.t    = 1 x v vector of test statistics, v=#vertices.
 slm.df   = degrees of freedom.
 slm.dfs  = 1 x v vector of optional effective degrees of freedom.
 slm.k    = #variates.
 mask     = 1 x v logical vector, 1=inside, 0=outside, 
          = ones(1,v), i.e. the whole surface, by default.
 The following are optional:
 slm.resl = e x k matrix of sum over observations of squares of
           differences of normalized residuals along each edge.
 slm.tri  = t x 3 matrix of triangle indices, 1-based, t=#triangles.
 or
 slm.lat  = 3D logical array, 1=in, 0=out.

 qval.Q    = 1 x v vector of Q-values.
 qval.mask = copy of mask.

0001 function qval = SurfStatQ( slm, mask );
0002 
0003 %Q-values for False Discovey Rate of resels.
0004 %
0005 % Usage: qval = SurfStatQ( slm [, mask] );
0006 %
0007 % slm.t    = 1 x v vector of test statistics, v=#vertices.
0008 % slm.df   = degrees of freedom.
0009 % slm.dfs  = 1 x v vector of optional effective degrees of freedom.
0010 % slm.k    = #variates.
0011 % mask     = 1 x v logical vector, 1=inside, 0=outside,
0012 %          = ones(1,v), i.e. the whole surface, by default.
0013 % The following are optional:
0014 % slm.resl = e x k matrix of sum over observations of squares of
0015 %           differences of normalized residuals along each edge.
0016 % slm.tri  = t x 3 matrix of triangle indices, 1-based, t=#triangles.
0017 % or
0018 % slm.lat  = 3D logical array, 1=in, 0=out.
0019 %
0020 % qval.Q    = 1 x v vector of Q-values.
0021 % qval.mask = copy of mask.
0022 
0023 [l,v]=size(slm.t);
0024 if nargin<2 | isempty(mask)
0025     mask=logical(ones(1,v));
0026 end
0027 
0028 df=zeros(2);
0029 ndf=length(slm.df);
0030 df(1,1:ndf)=slm.df;
0031 df(2,1:2)=slm.df(ndf);
0032 if isfield(slm,'dfs')
0033     df(1,ndf)=mean(slm.dfs(mask>0));
0034 end
0035 
0036 if isfield(slm,'du')
0037     [resels,reselspvert]=SurfStatResels(slm,mask);
0038 else
0039     reselspvert=ones(1,v);
0040 end
0041 reselspvert=reselspvert(mask);
0042 
0043 P_val=stat_threshold(0,1,0,df,[10 slm.t(1,mask)],[],[],[],slm.k,[],[],0);
0044 P_val=P_val(2:length(P_val));
0045 np=length(P_val);
0046 [P_sort, index]=sort(P_val);
0047 r_sort=reselspvert(index);
0048 c_sort=cumsum(r_sort);
0049 P_sort=P_sort./(c_sort+(c_sort<=0)).*(c_sort>0)*sum(r_sort);
0050 m=1;
0051 Q_sort=zeros(1,np);
0052 for i=np:-1:1
0053     if P_sort(i)<m
0054         m=P_sort(i);
0055     end
0056     Q_sort(i)=m;
0057 end
0058 Q=zeros(1,np);
0059 Q(index)=Q_sort;
0060 qval.Q=ones(1,size(mask,2));
0061 qval.Q(mask)=Q;
0062 qval.mask=mask;
0063 
0064 return
0065 end
0066