Corrmap: Difference between revisions

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===Purpose===
===Purpose===


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CORRMAP produces a pseudocolor map that shows the correlation between variables (columns) in a data set. The function will reorder the variables by KNN clustering if desired.
CORRMAP produces a pseudocolor map that shows the correlation between variables (columns) in a data set. The function will reorder the variables by KNN clustering if desired.


The input is the data data class "double" or "dataset".
The input is the data (data) class "double" or "dataset".
 
Optional input (''labels'') contains the variable labels when the data is class "double".


Optional input ''labels'' contains the variable labels when the data is class "double".
Optional input (''reord'') will cause CORRMAP to keep the original ordering of the variables if set to 0.


Optional input ''reord'' will cause CORRMAP to keep the original ordering of the variables if set to 0.
The output (order) is a vector of indices with the variable ordering.


The output order is a vector of indices with the variable ordering.
===Examples===


corrmap(data,labels) produces a psuedocolor correlation map with variable reordering.
corrmap(data,labels)
produces a pseudocolor correlation map with variable reordering.


corrmap(data,labels,0) produces a psuedocolor correlation map without variable reordering.
corrmap(data,labels,0)
produces a pseudocolor correlation map without variable reordering.


===See Also===
===See Also===


[[autocor]], [[crosscor]]
[[autocor]], [[crosscor]], [[xycorrcoef]]

Latest revision as of 14:59, 16 December 2019

Purpose

Correlation map with variable regrouping.

Synopsis

order = corrmap(data,labels,reord)
order = corrmap(data,reord)

Description

CORRMAP produces a pseudocolor map that shows the correlation between variables (columns) in a data set. The function will reorder the variables by KNN clustering if desired.

The input is the data (data) class "double" or "dataset".

Optional input (labels) contains the variable labels when the data is class "double".

Optional input (reord) will cause CORRMAP to keep the original ordering of the variables if set to 0.

The output (order) is a vector of indices with the variable ordering.

Examples

corrmap(data,labels)

produces a pseudocolor correlation map with variable reordering.

corrmap(data,labels,0)

produces a pseudocolor correlation map without variable reordering.

See Also

autocor, crosscor, xycorrcoef