Grouping data by value

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Nurul Ain Basirah Zakaria
Risposto: Walter Roberson il 8 Feb 2021
Hi. I have 432x1 data. I need to grouping the data.
for example;
-0.989088822111364
-1.16881053159657
-0.687960193675098
0.609028518151482
0.853112207247799
1.09867863373770
0.325581735572437
-0.358624505229866
-0.116184636997357
-1.12249570113502
-0.878711918410750
-0.843582079603111
-0.763107095944730
-0.963235098445007
-1.36135857090159
-1.93513836539865
-1.53179202986538
-1.24445835422832
0.643855927191538
0.506078478817649
1.10211477120999
0.119654919704736
-0.186087379373470
0.208608204079503
0.542441939121425
1.09907331469601
0.595582967612814
0.605674642059506
-0.0429774937224157
How can I assign them to groups such as:
moderate= -1.00 to -1.49
severe= -1.50 to -1.99

Risposta accettata

KSSV
KSSV il 8 Feb 2021
Let x be your data. Create arespective y-value which denotes grouping.
y = zeros(size(x)) ;
% get values -1.00 to -1.49
idx = x >= -1 && x < -1.49 ;
y(idx) = 1 ; % say one stand for moderate
% get values between -1.50 and -1.99
idx = x >= -1.50 && x < -1.99 ;
y(idx) = 2 ; % say two stand for

Più risposte (1)

Walter Roberson
Walter Roberson il 8 Feb 2021
discretize() with boundaries and 'categorical' and a list of category names.
data = [
-0.989088822111364
-1.16881053159657
-0.687960193675098
0.609028518151482
0.853112207247799
1.09867863373770
0.325581735572437
-0.358624505229866
-0.116184636997357
-1.12249570113502
-0.878711918410750
-0.843582079603111
-0.763107095944730
-0.963235098445007
-1.36135857090159
-1.93513836539865
-1.53179202986538
-1.24445835422832
0.643855927191538
0.506078478817649
1.10211477120999
0.119654919704736
-0.186087379373470
0.208608204079503
0.542441939121425
1.09907331469601
0.595582967612814
0.605674642059506
-0.0429774937224157];
cats = discretize(data, [-1.99, -1.50, -1, 1, 1.5, 1.99], 'categorical', {'severe', 'moderate', 'nomimal', 'good', 'excellent'})
cats = 29×1 categorical array
nomimal moderate nomimal nomimal nomimal good nomimal nomimal nomimal moderate nomimal nomimal nomimal nomimal moderate severe severe moderate nomimal nomimal good nomimal nomimal nomimal nomimal good nomimal nomimal nomimal

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