How to calaculate This standard deviation correction factor in Matlab?
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Hello guys,
i want to normalize my dta matrix by using the below formula.
could anyone help me to write the approriate piece of code for that, if that possible?
![Normalization_formula.jpg](https://www.mathworks.com/matlabcentral/answers/uploaded_files/261291/Normalization_formula.jpeg)
where g(i,j) is the value for feature i in sample j, sd(g(i)) is the standard deviation across samples for feature i, sd10(g) is the 10-percentile value of standard deviations across features.
My data matrix is looks like this which have 8 features and 16 samples in this example
![my_data_matrix.jpg](https://www.mathworks.com/matlabcentral/answers/uploaded_files/261292/my_data_matrix.jpeg)
I appreciate any help!
17 comentarios
Steven Lord
el 13 de En. de 2020
If the data is that large, how about posting a smaller subset of the data (say a dozen or so rows) and telling us what you would expect the answer to be for that smaller subset of data.
For example, let's take this sample dataset. What would you expect
to be for it?
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/261474/image.png)
rng default % Make sure we can each create the same A
A = randn(12, 8);
Respuestas (1)
Guillaume
el 13 de En. de 2020
I think you first need to clarify what is meant by "the 10-percentile value of standard deviations across features". I very much doubt it's the 10th of the standard deviation and obviously it's going to greatly influence your results.
However, since we don't know, here is the implementation with 1/10 of the standard deviation:
%input: g a NxM matrix, where rows are samples, columns are feature
result = g ./ (std(g, 0, 1) + 0.1*std(g, 0, 'all')); %R2018b or later
%result = g ./ (std(g, 0, 1) + 0.1*std(g(:))); %R2016b or later
%result = bsxfun(@rdivide, g, std(g, 0, 1) + 0.1*std(g(:))); %prior to R2016b
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