analysisEEG/stat_preConditionVectors.m at master · LightingResearchCenter/analysisEEG · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
% Calculates the means of the different variables stored in the output
% matrix (each matrix for one "analysis date"
function statOut = stat_preConditionVectors(checkupMatrix, aver_Period, handles)

    % for faster debugging / developing
    if nargin == 0
        handles = init_defaultSettings();
        close all
        load(fullfile(handles.path.debugMatFiles, 'tempLeventStats.mat'))
    else
        if handles.saveTempDebugMATs == 1
            save(fullfile(handles.path.debugMatFiles, 'tempLeventStats.mat'))
        end
    end

    % "depack" the needed variables from handles structure
    % defined in
    statFuncPath = handles.path.statFuncPath;
    shapWilk_pThr = handles.shapWilk_pThreshold;
    bartlett_pThr = handles.bartlett_pThreshold;

    % create the data vectors so that they are pushed for statistical
    % evaluation in a for-loop, the vectors should correspond to the
    % analyses done with Excel/SPSS previously

        % LIGHT
        % TRIAL
        % PERIOD
        % LIGHT * TRIAL
        % LIGHT * PERIOD
        % TRIAL * PERIOD
        % LIGHT * TRIAL * PERIOD

    % call a subfunction
    dataVectors = stat_createDataVectors(checkupMatrix, handles);

    % preallocate
    dataPermutations = cell(length(dataVectors),1);
    statOut = cell(length(dataVectors),1);

    %% Test all different combinations
    % created with the subfunction above
    for ij = 1 : length(dataVectors) % the size of this should be 7 for the "original analysis"
                                     % of Levent, see the combinations
                                     % above

        fieldIn = 'mean';
        [dataPermutations{ij}.data.(fieldIn), dataPermutations{ij}.colHeaders] = stat_createPermutations(dataVectors{ij}.data.(fieldIn), dataVectors{ij}.colHeaders);

        % check different permutations for given matrix
        [noOfDataPoints, numberOfCols] = size(dataPermutations{ij}.data.(fieldIn));
        numberOfCombinations = length(dataPermutations{ij}.data.(fieldIn).concatenated);

        for ik = 1 : numberOfCombinations

            % [ij ik]
            % number of columns equals to number of different combinations that you can have

            % check if the input data meets the criteria for the following tests
            indexForPowers = 1; % the second column is just a "group index"
            indexForSample = 2;
            statOut{ij}{ik}.tests = stat_statPreTests(dataPermutations{ij}.data.(fieldIn).concatenated{ik}(:,indexForPowers), ...
                                                      dataPermutations{ij}.data.(fieldIn).concatenated{ik}(:,indexForSample), ...
                                                      dataVectors{ij}.colHeaders{ik}, statFuncPath, shapWilk_pThr, bartlett_pThr);

            % do the actual tests
            statOut{ij}{ik}.tests = stat_statTestCollection(dataPermutations{ij}.data.(fieldIn).concatenated{ik}, ...
                                                          dataPermutations{ij}.data.(fieldIn).x1{ik}(:,indexForPowers), ...
                                                          dataPermutations{ij}.data.(fieldIn).x2{ik}(:,indexForPowers), ...
                                                          dataPermutations{ij}.colHeaders{ik}, ...
                                                          statOut{ij}{ik}.tests, statFuncPath, shapWilk_pThr, bartlett_pThr, handles);

            % save the header
            statOut{ij}{ik}.header = dataPermutations{ij}.colHeaders{ik};

            % Debug display, can be commented away if needed/wanted (both lines)
            % disp([ij ik])
            % dispStats = statOut{ij}{ik}.tests
            % header = statOut{ij}{ik}.header


        end
    end