This is the base class for generating objects with methods and storage components for computing and storing the correlation matrix of an input data.
More...

Inheritance diagram for Cor:

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Collaboration diagram for Cor:

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Public Member Functions
function	Cor (in df, in method)
	Return an object of class `Cor`. More...

function	get (in self, in df, in method)
	Return the correlation matrix of the input data. More...

Data Fields
Property	method

Property	val

Detailed Description

This is the base class for generating objects with methods and storage components for computing and storing the correlation matrix of an input data.

This is convenience class for easy computation of correlation and its storage all in one place.
The primary advantage of this class over the MATLAB intrinsic functions is in the ability of this class to compute the result for input dataframe table and return the results always in MATLAB table format.

Note: See the documentation of the class constructor below.

Final Remarks ⛓

If you believe this algorithm or its documentation can be improved, we appreciate your contribution and help to edit this page's documentation and source file on GitHub.
For details on the naming abbreviations, see this page.
For details on the naming conventions, see this page.
This software is distributed under the MIT license with additional terms outlined below.

If you use any parts or concepts from this library to any extent, please acknowledge the usage by citing the relevant publications of the ParaMonte library.
If you regenerate any parts/ideas from this library in a programming environment other than those currently supported by this ParaMonte library (i.e., other than C, C++, Fortran, MATLAB, Python, R), please also ask the end users to cite this original ParaMonte library.

This software is available to the public under a highly permissive license.
Help us justify its continued development and maintenance by acknowledging its benefit to society, distributing it, and contributing to it.

Copyright: Computational Data Science Lab

Author:: Joshua Alexander Osborne, May 21 2024, 4:16 AM, University of Texas at Arlington
Fatemeh Bagheri, May 20 2024, 1:25 PM, NASA Goddard Space Flight Center (GSFC), Washington, D.C.
Amir Shahmoradi, May 16 2016, 9:03 AM, Oden Institute for Computational Engineering and Sciences (ICES), UT Austin

Definition at line 23 of file Cor.m.

Constructor & Destructor Documentation

◆ Cor()

function Cor::Cor	(	in	df,
		in	method
	)

Return an object of class Cor.

This is the constructor of the Cor class.

Parameters

[in]	df	: The input MATLAB matrix or table of rank `2` containing the data as `ncol` columns of `nrow` observations whose correlation matrix must be computed. (optional. If missing, the correlation matrix will not be computed.)
[in]	method	: The input scalar MATLAB string that can be either: `"pearson"` : for computing the Pearson correlation matrix of the input data. `"kendall"` : for computing the kendall rank correlation matrix of the input data. `"spearman"` : for computing the Spearman rank correlation matrix of the input data. (optional, default = `"pearson"`)

Returns: self : The output object of class pm.stats.Cor.

Possible calling interfaces ⛓

: mat = pm.stats.Cor()

mat = pm.stats.Cor(df)

mat = pm.stats.Cor(df, method)

Cor::method
Property method
Definition: Cor.m:41

Example usage ⛓

: 1cd(fileparts(mfilename('fullpath'))); % Change working directory to source code directory.

2addpath('../../../'); % Add the ParaMonte library root directory to the search path.

3

4% Make a positive-definite random matrix.

5pm.matlab.show()

6pm.matlab.show("cholow = chol(pm.stats.dist.cov.getRand(5), 'lower');")

7 cholow = chol(pm.stats.dist.cov.getRand(5), 'lower');

8pm.matlab.show("df = pm.stats.dist.mvn.getRand(zeros(length(cholow), 1), cholow, 5000)';")

9 df = pm.stats.dist.mvn.getRand(zeros(length(cholow), 1), cholow, 5000)';

10

11pm.matlab.show()

12pm.matlab.show('c = pm.stats.Cor(df); c.val')

13 c = pm.stats.Cor(df); c.val

14

15pm.matlab.show()

16pm.matlab.show('c = pm.stats.Cor(df, "pearson"); c.val')

17 c = pm.stats.Cor(df, "pearson"); c.val

18

19pm.matlab.show()

20pm.matlab.show('c = pm.stats.Cor(df, "spearman"); c.val')

21 c = pm.stats.Cor(df, "spearman"); c.val

22

23pm.matlab.show()

24pm.matlab.show('c = pm.stats.Cor(df, "kendall"); c.val')

25 c = pm.stats.Cor(df, "kendall"); c.val

26

27pm.matlab.show()

28pm.matlab.show('p = pm.vis.PlotHeatmap(c.val); p.make("precision", 2); p.subplot.setColorLim();')

29 p = pm.vis.PlotHeatmap(c.val); p.make("precision", 2); p.subplot.setColorLim();

30pm.matlab.show('p.savefig("Cor.unifrnd.png", "-m3");')

31 p.savefig("Cor.unifrnd.png", "-m3");

root
function root()
Return a scalar MATLAB string containing the root directory of the ParaMonte library package.

Example output ⛓
1

2cholow = chol(pm.stats.dist.cov.getRand(5), 'lower');

3df = pm.stats.dist.mvn.getRand(zeros(length(cholow), 1), cholow, 5000)';

4

5c = pm.stats.Cor(df); c.val

6ans =

7 5x5 table

8 Var1 Var2 Var3 Var4 Var5

9 _________________ _________________ _________________ _________________ _________________

10 Var1 1 0.999665242214288 0.458249571370196 0.792715437534784 0.516374060655296

11 Var2 0.999665242214288 1 0.442021537667676 0.789882259020928 0.523298427619952

12 Var3 0.458249571370196 0.442021537667675 1 0.111302230708503 0.369318991921761

13 Var4 0.792715437534784 0.789882259020928 0.111302230708503 1 0.144750985785344

14 Var5 0.516374060655296 0.523298427619952 0.369318991921762 0.144750985785344 1

15

16c = pm.stats.Cor(df, "pearson"); c.val

17ans =

18 5x5 table

19 Var1 Var2 Var3 Var4 Var5

20 _________________ _________________ _________________ _________________ _________________

21 Var1 1 0.999665242214288 0.458249571370196 0.792715437534784 0.516374060655296

22 Var2 0.999665242214288 1 0.442021537667676 0.789882259020928 0.523298427619952

23 Var3 0.458249571370196 0.442021537667675 1 0.111302230708503 0.369318991921761

24 Var4 0.792715437534784 0.789882259020928 0.111302230708503 1 0.144750985785344

25 Var5 0.516374060655296 0.523298427619952 0.369318991921762 0.144750985785344 1

26

27c = pm.stats.Cor(df, "spearman"); c.val

28ans =

29 5x5 table

30 Var1 Var2 Var3 Var4 Var5

31 _________________ _________________ _________________ _________________ _________________

32 Var1 1 0.999614926128597 0.441025917961037 0.777405685336228 0.497278984627159

33 Var2 0.999614926128597 1 0.424880785923231 0.774544424133777 0.503945071197803

34 Var3 0.441025917961037 0.424880785923231 1 0.106632464969299 0.359281052995242

35 Var4 0.777405685336228 0.774544424133777 0.106632464969299 1 0.135622407600896

36 Var5 0.497278984627159 0.503945071197803 0.359281052995242 0.135622407600896 1

37

38c = pm.stats.Cor(df, "kendall"); c.val

39ans =

40 5x5 table

41 Var1 Var2 Var3 Var4 Var5

42 _________________ _________________ __________________ __________________ __________________

43 Var1 1 0.983398279655931 0.302229885977195 0.582272774554911 0.344105301060212

44 Var2 0.983398279655931 1 0.290476175235047 0.579331066213243 0.349104380876175

45 Var3 0.302229885977195 0.290476175235047 1 0.0712644928985797 0.24269125825165

46 Var4 0.582272774554911 0.579331066213243 0.0712644928985797 1 0.0910433286657331

47 Var5 0.344105301060212 0.349104380876175 0.24269125825165 0.0910433286657331 1

48

49p = pm.vis.PlotHeatmap(c.val); p.make("precision", 2); p.subplot.setColorLim();

50p.savefig("Cor.unifrnd.png", "-m3");

Visualization of the example output ⛓

Final Remarks ⛓

If you believe this algorithm or its documentation can be improved, we appreciate your contribution and help to edit this page's documentation and source file on GitHub.
For details on the naming abbreviations, see this page.
For details on the naming conventions, see this page.
This software is distributed under the MIT license with additional terms outlined below.

If you use any parts or concepts from this library to any extent, please acknowledge the usage by citing the relevant publications of the ParaMonte library.
If you regenerate any parts/ideas from this library in a programming environment other than those currently supported by this ParaMonte library (i.e., other than C, C++, Fortran, MATLAB, Python, R), please also ask the end users to cite this original ParaMonte library.

This software is available to the public under a highly permissive license.
Help us justify its continued development and maintenance by acknowledging its benefit to society, distributing it, and contributing to it.

Copyright: Computational Data Science Lab

Author:: Joshua Alexander Osborne, May 21 2024, 4:22 AM, University of Texas at Arlington
Fatemeh Bagheri, May 20 2024, 1:25 PM, NASA Goddard Space Flight Center (GSFC), Washington, D.C.
Amir Shahmoradi, May 16 2016, 9:03 AM, Oden Institute for Computational Engineering and Sciences (ICES), UT Austin

Member Function Documentation

◆ get()

function Cor::get	(	in	self,
		in	df,
		in	method
	)

Return the correlation matrix of the input data.

This is a dynamic method of the Cor class. This method automatically stores any input information in the corresponding components of the parent object.
However, any components of the parent object corresponding to the output of this method must be set explicitly manually.

Parameters

[in]	df	: The input MATLAB matrix or table of rank `2` containing the data as `ncol` columns of `nrow` observations whose correlation matrix must be computed.
[in]	method	: The input scalar MATLAB string that can be either: `"pearson"` : for computing the Pearson correlation matrix of the input data. `"kendall"` : for computing the kendall rank correlation matrix of the input data. `"spearman"` : for computing the Spearman rank correlation matrix of the input data. (optional, default = pm.stats.Cor.method)

Returns: val : The output MATLAB table containing the correlation matrix.

Possible calling interfaces ⛓

: mat = pm.stats.Cor()

mat.val = mat.get(df)

mat.val = mat.get(df, method)

Note: See the documentation of the class constructor pm.stats.Cor for example usage.

Final Remarks ⛓

If you believe this algorithm or its documentation can be improved, we appreciate your contribution and help to edit this page's documentation and source file on GitHub.
For details on the naming abbreviations, see this page.
For details on the naming conventions, see this page.
This software is distributed under the MIT license with additional terms outlined below.

If you use any parts or concepts from this library to any extent, please acknowledge the usage by citing the relevant publications of the ParaMonte library.
If you regenerate any parts/ideas from this library in a programming environment other than those currently supported by this ParaMonte library (i.e., other than C, C++, Fortran, MATLAB, Python, R), please also ask the end users to cite this original ParaMonte library.

This software is available to the public under a highly permissive license.
Help us justify its continued development and maintenance by acknowledging its benefit to society, distributing it, and contributing to it.

Copyright: Computational Data Science Lab

Author:: Joshua Alexander Osborne, May 21 2024, 4:24 AM, University of Texas at Arlington
Fatemeh Bagheri, May 20 2024, 1:25 PM, NASA Goddard Space Flight Center (GSFC), Washington, D.C.
Amir Shahmoradi, May 16 2016, 9:03 AM, Oden Institute for Computational Engineering and Sciences (ICES), UT Austin

Field Documentation

◆ method

Property Cor::method

method

The scalar MATLAB string containing the method of computing the correlation matrix.
It can be either:

"pearson" : for computing the Pearson correlation matrix of the input data.
"kendall" : for computing the kendall rank correlation matrix of the input data.
"spearman" : for computing the Spearman rank correlation matrix of the input data.

Definition at line 41 of file Cor.m.

◆ val

Property Cor::val

val

The MATLAB table of rank 2 serving as a convenient storage component for the correlation matrix.
This component is automatically populated at the time of constructing an object of class pm.stats.Cor.
It must be populated manually at all other times.

Definition at line 52 of file Cor.m.

The documentation for this class was generated from the following file:

src/matlab/main/+pm/+stats/Cor.m

Public Member Functions

Data Fields

Detailed Description

Constructor & Destructor Documentation

◆ Cor()

Member Function Documentation

◆ get()

Field Documentation

◆ method

◆ val