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: SPMCorrClusTh2.m
    path of file: ./repos/ExploreASL/WorkInProgress/StatsAnalysis/StatTestCompare
    
    the code of the file until where you have to start completion: function SPMCorrClusTh2( x, p1, GoldPos )
%SPMCorrClusTh2 Summary of this function goes here
%   Detailed explanation goes here

    clear matlabbatch
    matlabbatch{1}.spm.stats.results.spmmat                 = { fullfile( x.StatsCmpDir, 'SPM.mat') };
    matlabbatch{1}.spm.stats.results.conspec.titlestr       = '';
    matlabbatch{1}.spm.stats.results.conspec.contrasts      = 1;
    matlabbatch{1}.spm.stats.results.conspec.threshdesc     = 'none';
    matlabbatch{1}.spm.stats.results.conspec.thresh         = 0.5;
    matlabbatch{1}.spm.stats.results.conspec.mask.none      = 1;
    matlabbatch{1}.spm.stats.results.units                  = 1;
    matlabbatch{1}.spm.stats.results.print                  = false;

    
function SPMCorrClusTh2( x, p1, GoldPos )
%SPMCorrClusTh2 Summary of this function goes here
%   Detailed explanation goes here

    clear matlabbatch
    matlabbatch{1}.spm.stats.results.spmmat                 = { fullfile( x.StatsCmpDir, 'SPM.mat') };
    matlabbatch{1}.spm.stats.results.conspec.titlestr       = '';
    matlabbatch{1}.spm.stats.results.conspec.contrasts      = 1;
    matlabbatch{1}.spm.stats.results.conspec.threshdesc     = 'none';
    matlabbatch{1}.spm.stats.results.conspec.thresh         = 0.5;
    matlabbatch{1}.spm.stats.results.conspec.mask.none      = 1;
    matlabbatch{1}.spm.stats.results.units                  = 1;
    matlabbatch{1}.spm.stats.results.print                  = false;
%     matlabbatch{1}.spm.stats.results.write.binary.basename  = 'filter';
    matlabbatch{1}.spm.stats.results.write.tspm.basename = 'filter';
%     matlabbatch{1}.spm.stats.results.write.nary.basename = 'filter';

    %% Loop over cluster volume thresholds
    ItRes   = 100; % nSteps
    kMin    = 1; % minimal cluster size to search for
    kMax    = 1000000;

    for It=1:ItRes It=5
        StatCalc(It,1)                                      = round(((kMax-kMin)/ItRes)*It);
        matlabbatch{1}.spm.stats.results.conspec.extent     = StatCalc(It,1); % cluster-size threshold

        spm_jobman('run',matlabbatch);
        close
        close

        FilterFile          = fullfile( x.StatsCmpDir, 'spmT_0001_filter.nii');

        FilterTemp          = xASL_io_ReadNifti( FilterFile );
        FilterTemp          = FilterTemp.dat(:,:,:);

        FigureOut   = fullfile( x.StatsCmpDir, ['ROC_' num2str(iSub) '_subjects_vol_' num2str(ActivationVolume) '_CLUSTER.jpg']);
        [ StatCalc AUC(iVol,1)  ] = GetRocCurve( x, GoldPos, FigureOut, fullfile( x.StatsCmpDir, 'spmT_0001_filter.nii') );

        [StatCalc(It,2) StatCalc(It,3)]     = CalcROC( GoldPos, FilterTemp); % true positive rate, false positive rate
        delete( FilterFile );
    end