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_mvb_cvk_display.m
    path of file: ./repos/ExploreASL/External/SPMmodified
    
    the code of the file until where you have to start completion: function spm_mvb_cvk_display(MVB)
% model display for MVB with cross-validation
% FORMAT spm_mvb_cvk_display(MVB)
% MVB  - multivariate Bayes structure, select one if not provided
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
 
% Christophe Phillips
% $Id: spm_mvb_cvk_display.m 3806 2010-04-06 14:42:32Z ged $

if nargin<1

    
function spm_mvb_cvk_display(MVB)
% model display for MVB with cross-validation
% FORMAT spm_mvb_cvk_display(MVB)
% MVB  - multivariate Bayes structure, select one if not provided
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
 
% Christophe Phillips
% $Id: spm_mvb_cvk_display.m 3806 2010-04-06 14:42:32Z ged $

if nargin<1
    load(spm_select(1,'^MVB.*\.mat','Select MVB to display'))
end
if ~isfield(MVB,'cvk')
    error(['No crossvalidation data available. ' ...
        'Select another file or perform the crossvalidation']);
end

%-Get figure handles and set title
%--------------------------------------------------------------------------
Fmvb = spm_figure('GetWin','MVB');
spm_clf(Fmvb);
 
% get stuff in place for display
%--------------------------------------------------------------------------
K     = MVB.K;
X     = K*MVB.X;
X0    = orth(K*MVB.X0);
R     = speye(length(X)) - X0*X0';
R     = orth(R);
pX     = R*R'*X;
 
% plot validation
%--------------------------------------------------------------------------
subplot(2,2,1)
s      = 1:length(pX);
plot(s,pX,s,MVB.cvk.qX,'-.')
xlabel('sample')
ylabel('response (adjusted)')
title('cross-validation')
axis square
 
subplot(2,2,2)
plot(pX,MVB.cvk.qX,'.')
xlabel('true')
ylabel('predicted')
title(sprintf('p-value (parametric) = %.5f',MVB.p_value))
axis square
abc = axis;
hold on
plot([max(abc([1 3])) min(abc([2 4]))],[max(abc([1 3])) min(abc([2 4]))],'k')

% plot feature weights
%--------------------------------------------------------------------------
subplot(2,2,3)
imagesc(corrcoef(MVB.cvk.qE))
colorbar
caxis([0 1])
xlabel('bipartition (k)')
title({'correlations among';'k-fold feature weights'})
axis square
 
subplot(2,2,4)
if ~isempty(MVB.XYZ) && ~isempty(MVB.VOX)
    if isfield(MVB.cvk, 'P')
        spm_mip(prod(MVB.cvk.P,2),MVB.XYZ(1:3,:),MVB.VOX)
        title({[MVB.name ' (' MVB.contrast ')'];'prod( P(|weights| > 0) )'})
    else % reproduce plot from original spm_mvb_cvk2
        qe = mean(MVB.cvk.qE,2);
        qe = qe.*(qe > 0);
        spm_mip(qe,MVB.XYZ(1:3,:),MVB.VOX)
        title({[MVB.name ' (' MVB.contrast ')'];'mean (positive) weights'})
    end