Contains methods for analyzing multivariate samples.
Inheritance Hierarchy
Namespace:
Meta.Numerics.Statistics
Assembly:
Meta.Numerics (in Meta.Numerics.dll) Version: 4.1.4
Syntax public static class Multivariate
<ExtensionAttribute>
Public NotInheritable Class Multivariate
[ExtensionAttribute]
public ref class Multivariate abstract sealed
[<AbstractClassAttribute>]
[<SealedAttribute>]
[<ExtensionAttribute>]
type Multivariate = class end
The Multivariate type exposes the following members.
Methods |
| Name | Description |
|---|
  | MeansClustering |
Compute k-means clusters of the data.
|
| MultiLinearLogisticRegression(IReadOnlyListBoolean, IReadOnlyDictionaryString, IReadOnlyListDouble) |
Performs a multivariate linear logistic regression on the named columns.
|
| MultiLinearLogisticRegression(IReadOnlyListBoolean, IReadOnlyListIReadOnlyListDouble) |
Performs a multivariate linear logistic regression on the listed columns.
|
| MultiLinearLogisticRegression(IReadOnlyListBoolean, IReadOnlyListDouble) |
Performs a multivariate linear logistic regression.
|
| MultiLinearRegression(IReadOnlyListDouble, IReadOnlyDictionaryString, IReadOnlyListDouble) |
Performs a multivariate linear regression on the named columns.
|
| MultiLinearRegression(IReadOnlyListDouble, IReadOnlyListIReadOnlyListDouble) |
Performs a multivariate linear regression on the listed columns.
|
| MultiLinearRegression(IReadOnlyListDouble, IReadOnlyListDouble) |
Performs a multivariate linear regression.
|
| PrincipalComponentAnalysis(IReadOnlyListIReadOnlyListDouble) |
Performs a principal component analysis of the multivariate sample.
|
| PrincipalComponentAnalysis(IReadOnlyListDouble) |
Performs a principal component analysis of the columns.
|
| RawMoment(IReadOnlyListIReadOnlyListDouble, IReadOnlyListInt32) |
Computes the multivariate raw moment specified by the listed powers.
|
| RawMoment(IReadOnlyListIReadOnlyListDouble, Int32) |
Computes the specified multivariate raw moment.
|
TopRemarks A multivariate sample is one in which each element of the sample consists of measurements of
multiple quantities. For example, a survey in which we record the sex, income, education level,
and race of each participant is a multivariate sample. Note that the types of each quantity
measured need not be the same.
One common form of statistical analysis of a multivariate sample is to try to predict
one variable on the basis of some of the others. To do this you can use
MultiLinearRegression(IReadOnlyListDouble, IReadOnlyListDouble)
or, if the dependent variable is Boolean,
MultiLinearLogisticRegression(IReadOnlyListBoolean, IReadOnlyListDouble).
Another common operation of multivariate data is to try to organize the observations
into clusters, which you can do using
MeansClustering(IReadOnlyListIReadOnlyListDouble, Int32).
Instead of trying to reduce all the observations to a few important representatives,
you can try to reduce all the variables to a few important directions using
PrincipalComponentAnalysis(IReadOnlyListIReadOnlyListDouble).
See Also 
