Difference between revisions of "Korelácie"

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(New page: Data: scatter (x, y, [], sqrt(x.^2 + y.^2)); '''scatterplotf.m''' <source lang="octave"> function ret=scatterplotf(x,y) [m,n]=size(y); if m==1, y=y'; x=x'; end dispmat=[...)
 
 
(8 intermediate revisions by the same user not shown)
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Data:
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[http://www.dpmi.tugraz.at/schloegl/matlab/NaN/ The NaN-toolbox: A statistic-toolbox] for Octave and Matlab which
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handles data with and without MISSING VALUES encoded as NaN's.
  
scatter (x, y, [], sqrt(x.^2 + y.^2));
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Installing the NaN-tb with Octave:
  
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a) You need [http://users.powernet.co.uk/kienzle/octave/matcompat/scripts/general/repmat.m repmat.m]repmat.m (e.g. from P.Kienzle's MATCOMPAT)
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b) extract files from [http://www.dpmi.tugraz.at/schloegl/matlab/NaN/NaN.zip NaNnnn.tar.gz] and move them into
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<TT>.../octave/.../m/statistics/base/ </TT>
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c) (re-)start Octave and run <TT>naninsttest</TT>.
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This checks whether all previous functions have been replaced
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Data Set 1:
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X = [4.4 5.5 4.2 3.0 4.5 4.9 4.6 5.0 4.7 5.1 4.4 4.1 4.9 4.7 5.0 4.6 3.6 4.9 5.1 4.8 5.2 5.2]';
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Y = [12 14 18 35 23 29 16 12 18 21 27 13 19 22 20 16 27 21 13 18 17 11]';
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scatter (x, y, [], sqrt(x.^2 + y.^2));
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Data Set 2:
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V = [5.8 7.3 5.6 4.0 6.0 6.5 6.2 6.6 6.3 6.8 5.8 5.5 6.5 6.3 6.6 6.1 4.8 6.5 6.8 6.4 6.9 6.9]';
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T = [13 35 13 11 16 19 18 21 18 27 14 12 21 18 22 17 12 20 23 18 27 29]';
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scatter (V, T, 5, sqrt(V.^2 + T.^2));
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axis ([0 10 0 40]
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[[Image:ScatterPlot.png]]
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Data Set 3:
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[[Media:Cities.mat]]
  
 
'''scatterplotf.m'''
 
'''scatterplotf.m'''
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</source>
 
</source>
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Pokračovanie: [[PCA analýza akčných potenciálov]]

Latest revision as of 11:15, 12 November 2008

The NaN-toolbox: A statistic-toolbox for Octave and Matlab which handles data with and without MISSING VALUES encoded as NaN's.

Installing the NaN-tb with Octave:

a) You need repmat.mrepmat.m (e.g. from P.Kienzle's MATCOMPAT)

b) extract files from NaNnnn.tar.gz and move them into .../octave/.../m/statistics/base/

c) (re-)start Octave and run naninsttest. This checks whether all previous functions have been replaced



Data Set 1:

X = [4.4 5.5 4.2 3.0 4.5 4.9 4.6 5.0 4.7 5.1 4.4 4.1 4.9 4.7 5.0 4.6 3.6 4.9 5.1 4.8 5.2 5.2]';
Y = [12 14 18 35 23 29 16 12 18 21 27 13 19 22 20 16 27 21 13 18 17 11]';
scatter (x, y, [], sqrt(x.^2 + y.^2));


Data Set 2:

V = [5.8 7.3 5.6 4.0 6.0 6.5 6.2 6.6 6.3 6.8 5.8 5.5 6.5 6.3 6.6 6.1 4.8 6.5 6.8 6.4 6.9 6.9]';
T = [13 35 13 11 16 19 18 21 18 27 14 12 21 18 22 17 12 20 23 18 27 29]';
scatter (V, T, 5, sqrt(V.^2 + T.^2));
axis ([0 10 0 40]

ScatterPlot.png

Data Set 3:

Media:Cities.mat

scatterplotf.m

function ret=scatterplotf(x,y)
  [m,n]=size(y);
  if m==1,
    y=y';
    x=x';
  end
  dispmat=[x y];
  n=size(dispmat,2);
  dispmat=sortrows(dispmat,1);
  x=dispmat(:,1);
  y=dispmat(:,2:n);
  plot(x,y);
endfunction

Pokračovanie: PCA analýza akčných potenciálov