Description |
This function accepts a vector or matrix and detects the outlier values in the vector/matrix using Thopson's Tau method, which is based on the absolute deviation of each record from the mean of the entire vector/matrix, and fills the outliers with NaNs in the returned output.
The magnitude of Thompson's Tau value corresponding to the number of records in the input vector (m) or matrix (m*n) to the Standard Deviation (std) of the input vector/matrix is the rule to decide if any record is in the outliers. The mean, std and the magnitude of Thompson's Tau (tau*std) are calculated again after removal of each outlier. If the input is matrix, it will be converted to a vector before detecting the outliers, however, the output will be a matrix with the same m*n dimensions as input. Indexes of the outleirs also will be returned, where if the input was a vector, the index vector also will be a vector, however, if the input was a matrix, outlier indexes will be returned in a two-column matrix showing i,j indexes of the outliers (see examples below).
--Inputs:
X0: input vector or matrix which contains outleirs
num_outliers: number of outliers that should be removed from the input vector/matrix
--Outputs:
X: output vector/matrix with outliers (if any detected) turned to NaN
outliers_idx: the index(es) of any detected outliers, the more extreme
outliers will be detected first, so the first index refers to the most extreme outlier and so forth
--Theory of Thompson Tau method:
http://www.mne.psu.edu/me345/Lectures/Outliers.pdf
http://www.jstor.org/stable/2345543 (Thompson, 1985)
--Note: this function is an improvement based on Vince Petaccio, 2009: http://www.mathworks.com/matlabcentral/fileexchange/24885-remove-outliers
--Improvements:
1. Handleing NaNs in inputs
2. Number of outliers to be removed is restricted to a user defined maximum to avoid uncontrolled shrinking of input dataset
3. Filling outliers by NaNs to preserve original dimensions of the input vector/matrix; this is crucial when the input variable is supposed to be used with another variable with the same size (e.g., for plotting, regression calculations, etc.)
4. Indexes of the outliers that have been detected and removed are returned so that the user knows which records have been removed, and since the indexes are ordered from the most extreme (negative or positive) to less extreme outliers, user will know which point was in the farthest outliers.
5. Syntax and algorithm has been significantly improved, this includes the logic for detection of the outliers from the upper and lower limits. Logic to detect an outlier is solely based on the absolute distance of each record from the central point rather than detecting the outliers sequentially, which was the case in Vince Petaccio, 2009, where outliers were detected and removed by order of one from the upper and the next from the lower extremes. This code first arranges the extreme values (upper or lower) to one side of the sorted vector based on the absolute distance from the center (while preserving the original arrangment in the input vector) then removes the bottom line element if it meets outlier conditions. This process continues until num_outliers is reached.
6. This function is enhanced to handle both vectors and matrices.
7. Valuable feedback from the user community (especially a user under
the name of John D'Errico) helped to detect and fix some issues in the
algorithm, which were related to exceptions involved in detecting
special types of outliers (please refer to the comments section). These
issues are now fixed. However, this code won't be able to find outliers
in curvilinear fitted data (which was one of the issues raised). This is
because the underlying logic to detect the outliers in (modified)
Thompson's Tau method is deviation from the mean. Check the references
given above or a good statistical reference if you are not very
familiar with the concept of outliers removal. One thing you should
know is that no outliers is absolutely an outlier, it is always a
relative.
% --Examples:
% -Example 1. Vector input:
X0=[2.0, 3.0, -50.5, 4.0, 109.0, 6.0]
[X, outliers_idx] = outliers(X0, 2) %call function with vector input
% X =
% 2, 3, NaN, 4, NaN, 6
%
% outliers_idx =
% 5, 3
%
% -Example 2. Matrix input:
X0= [2.0, 3.0, -50.5, 4.0, 109.0, 6.0;
5.3, 7.0, 80.0, 2.0, NaN, 1.0;
5.1, 2.7, 3.8, 2.0, 3.5, 21.0]
[X, outliers_idx] = outliers(X0, 4) %call function with matrix input
% X =
% 2, 3, NaN, 4, NaN , 6;
% 5.3, 7, NaN, 2, NaN, 1;
% 5.1, 2.7, 3.8, 2, 3.5, NaN
%
% outliers_idx =
% %(i) (J) %annotated
% 1, 5;
% 2, 3;
% 1, 3;
% 3, 6; |