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iCAPs_v2/packages/spm12/spm_DEM_set.m
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spm_DEM_set.m
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function [varargout] = spm_DEM_set(DEM)
% Performs checks on DEM structures
% FORMAT [DEM] = spm_DEM_set(DEM)
%
% DEM.M - hierarchical model
% DEM.Y - response variable, output or data
% DEM.U - explanatory variables, inputs or prior expectation of causes
% DEM.X - confounds
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Karl Friston
% $Id: spm_DEM_set.m 5708 2013-10-22 09:20:59Z karl $
% check recognition model
% -------------------------------------------------------------------------
DEM.M = spm_DEM_M_set(DEM.M);
% check format of inputs and data
% -------------------------------------------------------------------------
try, DEM.Y = DEM.Y.y'; end
try, DEM.U = DEM.U.u'; end
% check whether data are specified explicitly or with a generative model
% -------------------------------------------------------------------------
try
N = size(DEM.Y,2);
catch
try
DEM.G = spm_DEM_M_set(DEM.G);
N = size(DEM.C,2);
catch
error('please specify data or inputs')
end
end
try
DEM.class;
catch
DEM.class = 'unknown';
end
% ensure model and data dimensions check
% -------------------------------------------------------------------------
try
if size(DEM.Y,1) ~= DEM.M(1).l
error('DCM and data are incompatible')
end
catch
if size(DEM.C,1) ~= DEM.M(end).l
error('DCM and causes are incompatible')
end
end
% Default priors and confounds
% -------------------------------------------------------------------------
n = DEM.M(end).l;
if ~isfield(DEM,'U')
DEM.U = sparse(n,N);
end
if ~isfield(DEM,'X')
DEM.X = sparse(0,N);
end
% transpose causes and confounds, if specified in conventional fashion
%--------------------------------------------------------------------------
if size(DEM.U,2) < N, DEM.U = DEM.U'; end
if size(DEM.X,2) < N, DEM.X = DEM.X'; end
% check prior expectation of causes (at level n) and confounds
%--------------------------------------------------------------------------
if ~nnz(DEM.U), DEM.U = sparse(n,N); end
if ~nnz(DEM.X), DEM.X = sparse(0,N); end
% ensure inputs and cause dimensions check
% -------------------------------------------------------------------------
if size(DEM.U,1) ~= DEM.M(end).l
error('DCM inputs and priors are not compatible')
end
% ensure causes and data dimensions check
% -------------------------------------------------------------------------
if size(DEM.U,2) < N
error('priors and data have different lengths')
end
% ensure confounds and data dimensions check
% -------------------------------------------------------------------------
if size(DEM.X,2) < N
error('confounds and data have different lengths')
end
% check length of time-series
%--------------------------------------------------------------------------
if N < DEM.M(1).E.n
error('Please ensure time-series is longer than embedding order')
return
end
% unpack DEM if necessary
% -------------------------------------------------------------------------
if nargout > 1
varargout{1} = DEM.M;
varargout{2} = DEM.Y;
varargout{3} = DEM.U;
varargout{4} = DEM.X;
else
varargout{1} = DEM;
end
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