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isoutlier

Find outliers in data

Syntax

TF = isoutlier(A)
TF = isoutlier(A,method)
TF = isoutlier(A,movmethod,window)
TF = isoutlier(___,dim)
TF = isoutlier(___,Name,Value)
[TF,lower,upper,center] = isoutlier(___)

Description

example

TF = isoutlier(A) returns a logical array whose elements are true when an outlier is detected in the corresponding element of A. By default, an outlier is a value that is more than three scaled median absolute deviations (MAD) away from the median. If A is a matrix or table, then isoutlier operates on each column separately. If A is a multidimensional array, then isoutlier operates along the first dimension whose size does not equal 1.

example

TF = isoutlier(A,method) specifies a method for determining outliers. For example, isoutlier(A,'mean') returns true for all elements more than three standard deviations from the mean.

example

TF = isoutlier(A,movmethod,window) specifies a moving method for determining local outliers according to a window length defined by window. For example, isoutlier(A,'movmedian',5) returns true for all elements more than three local scaled MAD from the local median within a sliding window containing five elements.

example

TF = isoutlier(___,dim) operates along dimension dim of A for any of the previous syntaxes. For example, isoutlier(A,2) operates on each row of a matrix A.

example

TF = isoutlier(___,Name,Value) specifies additional parameters for detecting outliers using one or more name-value pair arguments. For example, isoutlier(A,'SamplePoints',t) detects outliers in A relative to the corresponding elements of a time vector t.

example

[TF,lower,upper,center] = isoutlier(___) also returns the lower and upper thresholds and the center value used by the outlier detection method.

Examples

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Find the outliers in a vector of data. A logical 1 in the output indicates the location of an outlier.

A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
TF = isoutlier(A)
TF =

  1×15 logical array

   0   0   0   1   0   0   0   0   1   0   0   0   0   0   0

Define outliers as points more than three standard deviations from the mean, and find the locations of outliers in a vector.

A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
TF = isoutlier(A,'mean')
TF =

  1×15 logical array

   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0

Create a vector of data containing a local outlier.

x = -2*pi:0.1:2*pi;
A = sin(x);
A(47) = 0;

Create a time vector that corresponds to the data in A.

t = datetime(2017,1,1,0,0,0) + hours(0:length(x)-1);

Define outliers as points more than three local scaled MAD away from the local median within a sliding window. Find the locations of the outliers in A relative to the points in t with a window size of 5 hours. Plot the data and detected outliers.

TF = isoutlier(A,'movmedian',hours(5),'SamplePoints',t);
plot(t,A,t(TF),A(TF),'x')
legend('Data','Outlier')

Find outliers for each row of a matrix.

Create a matrix of data containing outliers along the diagonal.

A = magic(5) + diag(200*ones(1,5))
A =

   217    24     1     8    15
    23   205     7    14    16
     4     6   213    20    22
    10    12    19   221     3
    11    18    25     2   209

Find the locations of outliers based on the data in each row.

TF = isoutlier(A,2)
TF =

  5×5 logical array

   1   0   0   0   0
   0   1   0   0   0
   0   0   1   0   0
   0   0   0   1   0
   0   0   0   0   1

Create a vector of data containing an outlier. Find and plot the location of the outlier, and the thresholds and center value determined by the outlier method. The center value is the median of the data, and the upper and lower thresholds are three scaled MAD above and below the median.

x = 1:10;
A = [60 59 49 49 58 100 61 57 48 58];
[TF,lower,upper,center] = isoutlier(A);
plot(x,A,x(TF),A(TF),'x',x,lower*ones(1,10),x,upper*ones(1,10),x,center*ones(1,10))
legend('Original Data','Outlier','Lower Threshold','Upper Threshold','Center Value')

Input Arguments

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Input data, specified as a vector, matrix, multidimensional array, table, or timetable.

If A is a table, then its variables must be of type double or single, or you can use the 'DataVariables' name-value pair to list double or single variables explicitly. Specifying variables is useful when you are working with a table that contains variables with data types other than double or single.

If A is a timetable, then isoutlier operates only on the table elements. Row times must be unique and listed in ascending order.

Data Types: double | single | table | timetable

Method for determining outliers, specified as one of the following:

MethodDescription
'median'Returns true for elements more than three scaled MAD from the median. The scaled MAD is defined as c*median(abs(A-median(A))) where c=-1/(sqrt(2)*erfcinv(3/2)).
'mean'Returns true for elements more than three standard deviations from the mean. This method is faster but less robust than 'median'.
'quartiles'Returns true for elements more than 1.5 interquartile ranges above the upper quartile or below the lower quartile. This method is useful when the data in A is not normally distributed.
'grubbs'Applies Grubbs’s test for outliers, which iteratively removes one outlier per iteration based on hypothesis testing. This method assumes that the data in A is normally distributed.
'gesd'Applies the generalized extreme Studentized deviate test for outliers. This iterative method is similar to 'grubbs', but can perform better when there are multiple outliers masking each other.

Moving method for determining outliers, specified as one of the following:

MethodDescription
'movmedian'Returns true for elements more than three local scaled MAD from the local median over a window length specified by window.
'movmean'Returns true for elements more than three local standard deviations from the local mean over a window length specified by window.

Window length, specified as a positive integer scalar, a two-element vector of positive integers, a positive duration scalar, or a two-element vector of positive durations.

When window is a positive integer scalar, then the window is centered about the current element and contains window-1 neighboring elements. If window is even, then the window is centered about the current and previous elements. If window is a two-element vector of positive integers [b f], then the window contains the current element, b elements backward, and f elements forward.

When A is a timetable or 'SamplePoints' is specified as a datetime or duration vector, then window must be of type duration, and the windows are computed relative to the sample points.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | duration

Dimension to operate along, specified as a positive integer scalar. If no value is specified, then the default is the first array dimension whose size does not equal 1.

Consider a matrix A.

isoutlier(A,1) detects outliers based on the data in each column of A.

isoutlier(A,2) detects outliers based on the data in each row of A.

When A is a table or timetable, dim is not supported. isoutlier operates along each table or timetable variable separately.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: isoutlier(A,'mean','ThresholdFactor',4)

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Detection threshold factor, specified as the comma-separated pair consisting of 'ThresholdFactor' and a nonnegative scalar.

For methods 'grubbs' and 'gesd', the detection threshold factor is a scalar ranging from 0 to 1. Values close to 0 result in a smaller number of outliers and values close to 1 result in a larger number of outliers. The default detection threshold factor is 0.5.

For methods 'movmedian' and 'movmean', the detection threshold factor replaces the number of scaled MAD or standard deviations from the mean, which is 3 by default.

For the 'quartile' method, the detection threshold factor replaces the number of interquartile ranges, which is 1.5 by default.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Sample points, specified as the comma-separated pair consisting of 'SamplePoints' and a vector. The sample points represent the location of the data in A. Sample points do not need to be uniformly sampled. By default, the sample points vector is [1 2 3 ...].

Moving windows are defined relative to the sample points, which must be sorted and contain unique elements. For example, if t is a vector of times corresponding to the input data, then isoutlier(rand(1,10),'movmean',3,'SamplePoints',t) has a window that represents the time interval between t(i)-1.5 and t(i)+1.5.

When the sample points vector has data type datetime or duration, then the moving window length must have type duration.

Data Types: double | single | datetime | duration

Table variables, specified as the comma-separated pair consisting of 'DataVariables' and a variable name, a cell array of variable names, a numeric vector, a logical vector, or a function handle. The 'DataVariables' value indicates which columns of the input table to detect outliers in, and can be one of the following:

  • A character vector specifying a single table variable name

  • A cell array of character vectors where each element is a table variable name

  • A vector of table variable indices

  • A logical vector whose elements each correspond to a table variable, where true includes the corresponding variable and false excludes it

  • A function handle that takes the table as input and returns a logical scalar

The data type associated with the indicated variable must be double or single.

Example: 'Age'

Example: {'Height','Weight'}

Example: @isnumeric

Data Types: char | cell | double | single | logical | function_handle

Maximum outlier threshold, for the 'gesd' method only, specified as the comma-separated pair consisting of 'MaxNumOutliers' and a positive integer. The 'MaxNumOutliers' value specifies the maximum number of outliers returned by the 'gesd' method. For example, isoutlier(A,'gesd','MaxNumOutliers',5) returns no more than five outliers.

The default value for 'MaxNumOutliers' is the integer nearest to 10 percent of the number of elements in A. Setting a larger value for the maximum number of outliers can ensure that all outliers are detected, but at the cost of reduced computational efficiency.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Output Arguments

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Outlier indicator, returned as a vector, matrix, or multidimensional array. An element of TF is true when the corresponding element of A is an outlier and false otherwise. TF is the same size as A.

Data Types: logical

Lower threshold used by the outlier detection method, returned as a scalar, vector, matrix, multidimensional array, table, or timetable. For example, the lower value of the default outlier detection method is three MAD below the median of the input data. lower has the same size as A in all dimensions except for the operating dimension where the length is 1.

Data Types: double | single | table | timetable

Upper threshold used by the outlier detection method, returned as a scalar, vector, matrix, multidimensional array, table, or timetable. For example, the upper value of the default outlier detection method is three MAD above the median of the input data. upper has the same size as A in all dimensions except for the operating dimension where the length is 1.

Data Types: double | single | table | timetable

Center value used by the outlier detection method, returned as a scalar, vector, matrix, multidimensional array, table, or timetable. For example, the center value of the default outlier detection method is the median of the input data. center has the same size as A in all dimensions except for the operating dimension where the length is 1.

Data Types: double | single | table | timetable

More About

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Median Absolute Deviation

For a random variable vector A made up of N scalar observations, the median absolute deviation (MAD) is defined as

MAD = median(|Aimedian(A)|)

for i = 1,2,...,N.

Extended Capabilities

Introduced in R2017a

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