You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_dcm_ppd.m
    path of file: ./repos/spm12
    
    the code of the file until where you have to start completion: function [qE,qC,P] = spm_dcm_ppd(TEST,TRAIN,Y,X,field,iX)
% Posterior predictive density for empirical Bayes and DCM
% FORMAT [qE,qC,P] = spm_dcm_ppd(TEST,TRAIN,Y,X,field,i)
%
% TEST   - {1 [x M]} structure DCM array of new subject
% TRAIN  - {N [x M]} structure DCM array of (M) DCMs from (N) subjects
% --------------------------------------------------------------------
%     DCM{i}.M.pE - prior expectation of parameters
%     DCM{i}.M.pC - prior covariances of parameters
%     DCM{i}.Ep   - posterior expectations
%     DCM{i}.Cp   - posterior covariance
%
% Y      - known values of design matrix for the test subject
% X      - second level design matrix, where X(:,1) = ones(N,1) [default]
% field  - parameter fields in DCM{i}.Ep to optimise [default: {'A','B'}]
%          'All' will invoke all fields (these constitute random effects)
% iX     - column of design matrix to be predicted   [default: iX=2]
% 
% qE     - posterior predictive expectation
% qC     - posterior predictive covariances
% P      - posterior probability over unique values of X(:,2)
%__________________________________________________________________________
%
% This routine inverts a hierarchical DCM using variational Laplace and
% Bayesian model reduction. In essence, it optimises the empirical priors
% over the parameters of a training set of first level DCMs, using 
% between subject constraints specified in the design matrix X. These
% optimised empirical priors are then used to parameterise a model of
% between subject effects for a single (test) subject. Usually, the second
% level of the design matrix specifies group differences and the posterior
% predictive density over this group effect can be used for classification
% or cross validation. it is assumed that the unknown  explanatory
% variable in the design matrix  pertains to the second column unless

    
function [qE,qC,P] = spm_dcm_ppd(TEST,TRAIN,Y,X,field,iX)
% Posterior predictive density for empirical Bayes and DCM
% FORMAT [qE,qC,P] = spm_dcm_ppd(TEST,TRAIN,Y,X,field,i)
%
% TEST   - {1 [x M]} structure DCM array of new subject
% TRAIN  - {N [x M]} structure DCM array of (M) DCMs from (N) subjects
% --------------------------------------------------------------------
%     DCM{i}.M.pE - prior expectation of parameters
%     DCM{i}.M.pC - prior covariances of parameters
%     DCM{i}.Ep   - posterior expectations
%     DCM{i}.Cp   - posterior covariance
%
% Y      - known values of design matrix for the test subject
% X      - second level design matrix, where X(:,1) = ones(N,1) [default]
% field  - parameter fields in DCM{i}.Ep to optimise [default: {'A','B'}]
%          'All' will invoke all fields (these constitute random effects)
% iX     - column of design matrix to be predicted   [default: iX=2]
% 
% qE     - posterior predictive expectation
% qC     - posterior predictive covariances
% P      - posterior probability over unique values of X(:,2)
%__________________________________________________________________________
%
% This routine inverts a hierarchical DCM using variational Laplace and
% Bayesian model reduction. In essence, it optimises the empirical priors
% over the parameters of a training set of first level DCMs, using 
% between subject constraints specified in the design matrix X. These
% optimised empirical priors are then used to parameterise a model of
% between subject effects for a single (test) subject. Usually, the second
% level of the design matrix specifies group differences and the posterior
% predictive density over this group effect can be used for classification
% or cross validation. it is assumed that the unknown  explanatory
% variable in the design matrix  pertains to the second column unless
% otherwise specified
%
% See also: spm_dcm_peb.m and spm_dcm_loo.m
%__________________________________________________________________________
% Copyright (C) 2015 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: spm_dcm_ppd.m 6737 2016-03-03 12:05:51Z karl $


% Set up
%==========================================================================

% explanatory variable (between subject effect) of interest
%--------------------------------------------------------------------------
if nargin < 6;
    iX    = 2;
end