DescriptiveStatisticsMatrix Methods |
The DescriptiveStatisticsMatrix type exposes the following members.
Methods| Name | Description | |
|---|---|---|
  | Center(Matrix) |
Center each series in specified data matrix.
Columns corresponds to variables
Rows corresponds to observations.
|
| Center(Matrix, Vector) |
Center each series in specified data matrix.
Columns corresponds to variables
Rows corresponds to observations.
|
| CholeskyWhite(Matrix) |
White series in specified data matrix.
I.e. center apply linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation use inversed Cholesky factor.
|
| CholeskyWhite(Matrix, Matrix) |
White series in specified data matrix. Use user specified covariance matrix instead of actual calculation.
I.e. center apply linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation use inversed Cholesky factor.
|
| CholeskyWhite(Matrix, Matrix, Matrix) |
White series in specified data matrix. Use user specified covariance matrix instead of actual calculation.
I.e. center apply linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation use inversed Cholesky factor.
|
| Correlation |
Returns correlation.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Correlation
|
| CorrelationAndMean |
Calculates mean and correlation for data matrix.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Correlation
|
| Covariance |
Returns population covariance.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CovarianceAndMean |
Calculates mean and population covariance for data matrix.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CovarianceAndMeanPopulation |
Calculates mean and population covariance for data matrix.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CovarianceAndMeanSample |
Calculates mean and estimation of sample covariance for data matrix.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CovariancePopulation |
Returns population covariance.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CovarianceSample |
Returns estimation of sample covariance.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CrossCorrelation |
Calculates mean and cross-correlation between two vector sets.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Correlation
|
| CrossCorrelationAndMean |
Calculates mean and cross-correlation between two vector sets.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Correlation
|
| CrossCovariance |
Calculates mean and population cross-covariance between two vector sets.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CrossCovarianceAndMean |
Calculates mean and population cross-covariance between two vector sets.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CrossCovarianceAndMeanPopulation |
Calculates mean and population cross-covariance between two vector sets.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CrossCovarianceAndMeanSample |
Calculates mean and sample cross-covariance between two vector sets.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CrossCovariancePopulation |
Calculates mean and population cross-covariance between two vector sets.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| CrossCovarianceSample |
Calculates mean and sample cross-covariance between two vector sets.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Covariance
|
| EnsureBoundaries |
Values below leftBoundary will be replaced with leftBoundary value.
Values above rightBoundary will be replaced with rightBoundary value.
|
| EnsureLeftBoundary |
Values below boundary will be replaced with boundary value.
|
| EnsureRightBoundary |
Values above boundary will be replaced with boundary value.
|
| GetCentered(Matrix) |
Get centered copy of data matrix.
Columns corresponds to variables
Rows corresponds to observations.
|
| GetCentered(Matrix, Matrix) |
Get centered copy of data matrix.
Columns corresponds to variables
Rows corresponds to observations.
|
| GetCentered(Matrix, Vector) |
Get centered copy of data matrix.
Columns corresponds to variables
Rows corresponds to observations.
|
| GetCentered(Matrix, Matrix, Vector) |
Get centered copy of data matrix.
Columns corresponds to variables
Rows corresponds to observations.
|
| GetCholeskyWhited(Matrix) |
Get whited copy of data matrix.
I.e. centered and after applying linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation use inversed Cholesky factor.
|
| GetCholeskyWhited(Matrix, Matrix) |
Get whited copy of data matrix.
I.e. centered and after applying linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation use inversed Cholesky factor.
|
| GetCholeskyWhited(Matrix, Matrix, Matrix) |
Get whited copy of data matrix. Use user specified covariance matrix instead of actual calculation.
I.e. centered and after applying linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation use inversed Cholesky factor.
|
| GetCholeskyWhited(Matrix, Matrix, Matrix, Matrix) |
Get whited copy of data matrix. Use user specified covariance matrix instead of actual calculation.
I.e. centered and after applying linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation use inversed Cholesky factor.
|
| GetEnsuredBoundaries(Matrix, Double, Double) |
Returns copy of matrix.
Values below leftBoundary will be replaced with leftBoundary value.
Values above rightBoundary will be replaced with rightBoundary value.
|
| GetEnsuredBoundaries(Matrix, Double, Double, Matrix) |
Store copy of matrix.
Values below leftBoundary will be replaced with leftBoundary value.
Values above rightBoundary will be replaced with rightBoundary value.
|
| GetEnsuredLeftBoundary(Matrix, Double) |
Returns copy of matrix. Values below boundary will be replaced with boundary value.
|
| GetEnsuredLeftBoundary(Matrix, Double, Matrix) |
Store copy of matrix. Values below boundary will be replaced with boundary value.
|
| GetEnsuredRightBoundary(Matrix, Double) |
Returns copy of matrix. Values above boundary will be replaced with boundary value.
|
| GetEnsuredRightBoundary(Matrix, Double, Matrix) |
Store copy of matrix. Values above boundary will be replaced with boundary value.
|
| GetStandardized(Matrix) |
Get standardized copy of data matrix.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Standardize
|
| GetStandardized(Matrix, Matrix) |
Get standardized copy of data matrix.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Standardize
|
| GetWhited(Matrix) |
Get whited copy of data matrix.
I.e. centered and after applying linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation power singular values of covariance matrix.
http://en.wikipedia.org/wiki/Whitening_transformation
|
| GetWhited(Matrix, Matrix) |
Get whited copy of data matrix.
I.e. centered and after applying linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation power singular values of covariance matrix.
http://en.wikipedia.org/wiki/Whitening_transformation
|
| GetWhited(Matrix, Matrix, Matrix) |
Get whited copy of data matrix. Use user specified covariance matrix instead of actual calculation.
I.e. centered and after applying linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation power singular values of covariance matrix.
http://en.wikipedia.org/wiki/Whitening_transformation
|
| GetWhited(Matrix, Matrix, Matrix, Matrix) |
Get whited copy of data matrix. Use user specified covariance matrix instead of actual calculation.
I.e. centered and after applying linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation power singular values of covariance matrix.
http://en.wikipedia.org/wiki/Whitening_transformation
|
| Maximum(Matrix) |
Returns maximum value for each data series.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Maxima_and_minima
|
| Maximum(Matrix, Vector) |
Calculates maximum value for each data series.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Maxima_and_minima
|
| Mean(Matrix) |
Returns mean for each data series.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Mean
|
| Mean(Matrix, Vector) |
Calculates mean for each data series.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Mean
|
| Minimum(Matrix) |
Returns minimum value for each data series.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Maxima_and_minima
|
| Minimum(Matrix, Vector) |
Calculates minimum value for each data series.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Maxima_and_minima
|
| MinimumMaximum(Matrix) |
Returns minimum and maximum values for each data series.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Maxima_and_minima
|
| MinimumMaximum(Matrix, IListDescriptiveStatisticsMinimumMaximumValuesDouble) |
Calculates minimum and maximum values for each data series.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Maxima_and_minima
|
| Standardize |
Standardize all series in data matrix.
Columns corresponds to variables
Rows corresponds to observations.
http://en.wikipedia.org/wiki/Standardize
|
| Sum(Matrix) |
Calculates sum of rows.
|
| Sum(Matrix, Vector) |
Calculates sum of rows.
|
| SumSquares(Matrix) |
Calculates sum squared values in rows.
|
| SumSquares(Matrix, Vector) |
Calculates sum squared values in rows.
|
| White(Matrix) |
White series in specified data matrix. I.e. center apply linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation power singular values of covariance matrix.
http://en.wikipedia.org/wiki/Whitening_transformation
|
| White(Matrix, Matrix) |
White series in specified data matrix. Use user specified covariance matrix instead of actual calculation.
I.e. center apply linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation power singular values of covariance matrix.
http://en.wikipedia.org/wiki/Whitening_transformation
|
| White(Matrix, Matrix, Matrix) |
White series in specified data matrix. Use user specified covariance matrix instead of actual calculation.
I.e. center apply linear transformation which intends to make covariance matrix unit.
Columns corresponds to variables
Rows corresponds to observations.
This transformation power singular values of covariance matrix.
http://en.wikipedia.org/wiki/Whitening_transformation
|
See Also