mean value of a group of data with NaNs
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I have a matrix A with the dimension of 12x360x180. It stores 12 values at each grid of the 360x180. Now I want to calculate the average value at each grid point. The problem is that there are unknown number of NaNs. Sometimes all 12 values are non-NaNs, sometimes all of them are NaNs, sometimes a portion of the 12 values are NaNs.
In this case, how do I estimate the mean values at each point of the grid? I know if there are no NaNs, the calculation is as easy as B = mean(A);
Thank you.
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the cyclist
le 27 Juin 2016
Modifié(e) : the cyclist
le 27 Juin 2016
If you have the Statistics and Machine Learning Toolbox, you can use the nanmean function, which computes the mean while ignoring NaNs.
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Chris Turnes
le 27 Juin 2016
You can also just use the 'omitnan' option in "mean":
A = [1 0 0 1 NaN 1 NaN 0];
M = mean(A,'omitnan')
M =
0.5000
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Chris Turnes
le 27 Juin 2016
By the way, other related functions support this input argument as well: sum, var, std, median, and max and min (max and min have 'omitnan' as the default option). The mov* functions (if you have R2016a or newer) also support this argument.
the cyclist
le 27 Juin 2016
If you do not have that Statistics and Machine Learning Toolbox, this should work:
notNan = not(isnan(A));
B = zeros(size(A));
B(notNan) = A(notNan);
sum(B)./sum(notNan)
You can do the sum over different dimensions, as required.
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