MATLAB: Splitapply table for zscore

splitapplyzscore

I have a table in the format:

Group Score
A 3
B 7
C 18
A 10

How do I combine the splitapply function for table variables with zscore , such that it calculates zscore(Score) according to Group rather than the whole table?

I have tried:

[G,results] = findgroups(Table.Group);
ZS = splitapply(@zscore,Table.Score,G);
results.ZS = ZS

Best Answer

splitapply is more for computing scalar summaries. Try using varfun, with a grouping variable, returning a table. Something like

t2 = varfun(@zscore,t,'GroupingVariable','Group')

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table function has VariableNames option, which can be used as follows:

array1=[1;2];
array2=[3;4];
array3=[5;6];
table(array1,array2,array3,'VariableNames',{'array1','array2','array3'})

MATLAB: How to run function 1000 times to find minimum

Something like

S = struct( 'score', cell(1,1000), 'grouping', cell(1,1000) );
for jj = 1 :1000
    S(jj).grouping = randomized_grouping_of_names();
    S(jj).score = main_function( S(jj).grouping );
end
[~,ix_min] = min([S.score]);
S(ix_min)

In response to comment

What's in the file, FileName ?

FileName  = ???
groupsize = ???
data = readtable( FileName, 'ReadRowNames', true );
number_of_people = size(data, 2);
number_of_groups = ceil( number_of_people / groupsize );
S = struct( 'score', cell(1,N), 'grouping', cell(1,N) );
for jj = 1 : N
    S(jj).grouping = generate_random_grouping( data, number_of_people, number_of_groups );
    S(jj).score = score_grouping( data, S(jj).grouping, ~ );
end
[~,ix_min] = min([S.score]);
S(ix_min)

No good.

With substatial help from Walter

>> subject_scores = Jeon_clust( 'Biol_528_2019_sheet.xlsx', 3 )
subject_scores = 
  struct with fields:
       score: 0.66667
    grouping: {1×8 cell}

where

function    S = Jeon_clust( FileName, groupsize )
    N = 1000;
    data = readtable( FileName, 'ReadRowNames', true );
    number_of_people = size(data, 2);
    number_of_groups = ceil( number_of_people / groupsize );
    S = struct( 'score', cell(1,N), 'grouping', cell(1,N) );
    for jj = 1 : N
        S(jj).grouping = generate_random_grouping( data, number_of_people, number_of_groups );
        S(jj).score = sum_total_subject_score( data, S(jj).grouping, number_of_groups );
    end
    [~,ix_min] = min([S.score]);
    S = S(ix_min);
end

and

function    stss = sum_total_subject_score( data, grouping, number_of_groups )
    subject_scores = score_grouping(data, grouping,number_of_groups);
    group_ = [subject_scores{1:number_of_groups}];
    group_scores = group_.';
    diff_score = diff(group_scores);
    total_subject_score = abs(sum(diff_score));
    stss = sum(total_subject_score);
end

Best Answer

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