Hi Gilles,
On Tue, Nov 06, 2012 at 03:06:07PM +0100, Martin Rosellen wrote:
Hi,
I analyse blood tests and not every blood sample is analysed for the
same values. It would be best if rows (tuples) that contain a NaN
are ignored.
It would be dangerous if Commons Math would simply discard NaN values as
they could have occurred because of a bug (that would go unnoticed).
The best thing would be to filter your data before attempting to analyze
them. The "to-be-ignored" property is indeed application-dependent, and so
is your choice of NaN to represent data that require special handling.
Best regards,
Gilles
That is interesting, because the SpearmanCorrelation does work with
NaNs. The NaNs somehow get a rank and make influence therefore the
coeffizient.
I also found a nanStrategy in the method rank in the class
NaturalRanking. But if I set the nanStrategy to REMOVED I get an exception.
Regards
Martin
Hello.
I am having difficulties using the Pearson Correlation because it
seems that it does not work if some cell is NaN. Is that intended?
Very likely. [When NaN appears in a computation, it propagates and the
result is NaN.]
Here is some code:
public static void main(String[] args) {
double [] row1 = new double[]{3,4};
double [] row2 = new double[]{1,8};
double [] row3 = new double[]{Double.NaN,4};
double[][] data = new double[][]{row1,row2,row3};
System.out.println(Arrays.deepToString(data));
PearsonsCorrelation coefMatrixP = new PearsonsCorrelation(data);
System.out.println(coefMatrixP.getCorrelationMatrix().toString());
}
What would you suggest should happen?
Regards,
Gilles
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