How to find variance and std in matlab without using zeros in matrix?

14 vues (au cours des 30 derniers jours)
Leesy
Leesy le 22 Fév 2017
Commenté : Rik le 12 Jan 2018
I have a matrix (pm2d), and i need to calculate the std and (population) variance in each column without using the zero values. I was wondering if i could use a for loop or an if statement?
For my variance i used:
var = sum(pm2d.^2)/(length(pm2d)-1) - (length(pm2d))*mean(pm2d).^2/(length(pm2d)-1)
But that took the zeros into account...
And for the standard deviation i used:
S = std(pm2d)
which definitely used the zeros.
Every code i try to write is not working. Any assistance would be appreciated! Thanks!

Réponse acceptée

Vandana Rajan
Vandana Rajan le 22 Fév 2017
Modifié(e) : Vandana Rajan le 22 Fév 2017
Hi,
You can use nanvar and nanstd functions in statistics toolbox.
>> b = pm2d; % just to retain the original matrix
>> b(b==0) = NaN;
>> nz_var = nanvar(b);
>> nz_std = nanstd(b);
Of course, this solution works only if you have license to statistics toolbox.
  2 commentaires
Leesy
Leesy le 22 Fév 2017
I do not, is there another way to do it?
Rik
Rik le 22 Fév 2017
Modifié(e) : Rik le 12 Jan 2018
Yes. My solution, or the one Jan Simon suggested (which should have better performance).

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Plus de réponses (2)

Rik
Rik le 22 Fév 2017
In my solution, I abuse the option of omitting NaNs when using mean and std
pm2d_temp=pm2d;%create a copy
pm2d_temp(pm2d_temp==0)=NaN;%overwrite zeroes with NaN
var = sum(pm2d.^2)/(length(pm2d)-1) - (length(pm2d))*mean(pm2d_temp,'omitnan').^2/(length(pm2d)-1)
S=std(pm2d_temp,'omitnan');

Jan
Jan le 22 Fév 2017
Modifié(e) : Jan le 22 Fév 2017
It works with replacing the zeros by NaNs and ignoring the NaNs, but you can do this directly also:
function [m, v, s] = StatsNonZeros(x, dim)
if nargin < 2 % Default: first non-singelton dimension
dimv = [find(size(x) ~= 1), 1]; %#ok<MXFND>
dim = dimv(1);
end
n = sum(x ~= 0, dim); % Number of non zero elements along dim
m = sum(x, dim) ./ n; % Zeros are neutral in the sum
v = sum(bsxfun(@minus, x, m) .^ 2, dim) ./ (n - 1);
s = sqrt(v);
end
This is what happens inside nanmean and nanstd also, after the NaNs have been replaced by zeros. Therefore it is an indirection to replace the zeros by NaNs at first.
Call it as:
[m,v,s] = StatsNonZero(pm2d)
  2 commentaires
Franck Eitel
Franck Eitel le 29 Nov 2017
What's the meaning of 's' here? I think we were looking for the variance and standard deviation. Pls could you clarify it for me?
Rik
Rik le 12 Jan 2018
[m, v, s] are the mean, variance, and standard deviation, although I presume you will have found that by now.

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