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

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!

 Accepted Answer

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 Comments

I do not, is there another way to do it?
Yes. My solution, or the one Jan Simon suggested (which should have better performance).

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More Answers (2)

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');
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This is what happens inside nanmean and nanstd also, after the NaNs have been replaced by zeros.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 Comments

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?
[m, v, s] are the mean, variance, and standard deviation, although I presume you will have found that by now.

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Asked:

on 22 Feb 2017

Commented:

Rik
on 12 Jan 2018

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