nanmean different outcomes?

Hi,

I'm doing some analysis on data that has the following time intervals, however when I calculate the mean on two different ways (just for sanity) I ran into the following problem.

I have two arrays:

A = [NaN	1,57031250000000	1,60156250000000	1,71093750000000	1,65234375000000	1,51367187500000	1,49804687500000	1,49414062500000	1,59179687500000	1,41015625000000];
nanmean(A) = 1.5603

B = [NaN	1,64648437500000	0,796875000000000	0,667968750000000	1,65820312500000	1,69140625000000	1,39062500000000	1,66015625000000	1,40039062500000	1,57226562500000	1,65429687500000];
nanmean(B) = 1.4139

Now I have put them behind each other in a [A B] way.

C = [A B];
nanmean(C) = 1.4832

However this mean is not the same as the mean of A and B done separately.

(1.5603 + 1.4139)/2 = 1.4871

How is this possible and why is it different? Is this because of the NaNs?

Answer 1

[Alex Mcaulley]

Your two arrays don't have the same number of non nan elements (10 vs 11), then the mean is

(1.5603*sum(~isnan(A)) + 1.4139*sum(~isnan(B)))/(sum(~isnan(A))+sum(~isnan(B))) = 1.4832

[JamJan]

Thank you, indeed very logical!