Difference between revisions of "SampleCorrelation"
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| − | [[ | + | [[Category: Statistical Functions]] |
| − | [[Category:Doc Status C]] <!-- For Lumina use, do not change --> | + | [[Category: Multivariate Distributions library functions]] |
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== SampleCorrelation(X, I, J, R) == | == SampleCorrelation(X, I, J, R) == | ||
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== Library == | == Library == | ||
| − | + | Multivariate Distributions library functions ([[media:Multivariate Distributions.ana |Multivariate Distributions.ana]]) | |
| + | :Use [[File menu|File]] → '''Add Library...''' to add this library | ||
== Notes == | == Notes == | ||
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* [[SampleCovariance]] | * [[SampleCovariance]] | ||
* [[Correlation]] | * [[Correlation]] | ||
| + | * [[Multivariate distributions]] | ||
Latest revision as of 22:09, 24 May 2016
SampleCorrelation(X, I, J, R)
Returns a correlation matrix based on data in «X», where each data point is a vector indexed by «I», and the entries in the correlation matrix are the pair-wise correlations of the columns of data. A second index, «J», of size identical to «I», is required in order to index the 2-dimensional result.
- SampleCorrelation(X : array[I, R]; I, J, R: IndexType)
Library
Multivariate Distributions library functions (Multivariate Distributions.ana)
- Use File → Add Library... to add this library
Notes
You can also use the built-in function Correlation to compute a correlation matrix. The built-in function is actually more flexible since it can also be used with sample weighting. The equivalent of SampleCorrelation(X, I, J, R) is:
Correlation(X, X[I = J], R)
See Also
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