MATLAB: How to calculate pearson correlation coefficient for more than one sample

pearson correlation coefficeint

Hi,

I want to calculate Pearson correlation coefficient of "1000_at" with other samples in the given example, How I should do it? I searched relevant questions but getting results for data sets in which only two samples are present. I am new to statistics and programming, please help me to calculate or redirect me to relevant posts, Thank you.

Best Answer

If M is your matrix, the column-wise correlations are in vector Rc:

Rm = corrcoef(M);
Rc = Rm(1,:)

The first entry of Rc is 1 because it is perfectly correlated with itself.

Related Solutions

MATLAB: Formula for cross-correlation in matlab

It looks like that 'xcorr' does not calculate Pearson correlation coefficient directly. To get Pearson correlation coefficient, we have to remove the mean first and then call 'xcorr' which will then give Pearson correlation coefficient.

MATLAB: Pearson correlation coefficient determination

What do you expect? It seems you keep on doing these computations, but then fail to think about the result, not thinking why you get what you did. For example...

corrcoef(Absorbance1, Absorbance2)
ans =
                         1         0.999999780102625
         0.999999780102625                         1

The coreelation coefficient is NOT 1. However, it is very near 1. Not exactly so though.

p1 = polyfit(Absorbance1',Absorbance2',1)
p1 =
          2.50345381465648       -0.0079603101543761
          
[Absorbance1'*p1(1) + p1(2), Absorbance2']
ans =
       0.00319199569957401       0.00396857486191138
         0.580997733409916         0.580941962999683
          1.15897081349257          1.15836071715381
          1.73713188545425          1.73637553044848
          2.31550417871278          2.31518905653607
          2.89411383324176           2.8950745980109

So, if we transform Absorbance1 by a linear transformation, we get something virtually identical to Absorbance2.

Likewise, C is a perfectly linear sequence.

C
C =
     0    10    20    30    40    50
diff(Absorbance1)
ans =
         0.230803434170655         0.230870278772036         0.230945371780708         0.231029743737411         0.231124557258262

As you should see by the differences there, Absorbance1 is nearly so too.

Can you possibly expect to not see nearly unit correlations for each of those comparisons? Two perfectly linear (non-constant) sequences will have a correlation coefficient that is either 1 or -1.

Look at what you get. Don't just compute a number and assume it has any meaning. Think about what you have done. Does what you did make sense?

Best Answer

Related Solutions

MATLAB: Formula for cross-correlation in matlab

MATLAB: Pearson correlation coefficient determination

Related Question