# Difference between revisions of "Distslct"

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===Purpose=== | ===Purpose=== | ||

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===Description=== | ===Description=== | ||

− | DISTSLCT first identifies a sample in the ''M'' by ''N'' data set x furthest from the data set mean. Subsequent samples are selected to be simultaneously the furthest from the mean and the selected samples for a total of nosamps selected samples. DISTSLCT calls STDSSLCT to find the number of samples up to the rank of the data and uses a distance measure to find additional samples if nosamps>rank(x). | + | DISTSLCT first identifies a sample in the ''M'' by ''N'' data set x furthest from the data set mean. Subsequent samples are selected to be simultaneously the furthest from the mean and the selected samples for a total of <tt>nosamps</tt> selected samples. DISTSLCT calls STDSSLCT to find the number of samples up to the rank of the data and uses a distance measure to find additional samples if <tt>nosamps>rank(x)</tt>. |

− | Optional intput tells DISTSLCT how many samples STDSLCT should estimate when nosamps>''N'': | + | Optional intput tells DISTSLCT how many samples STDSLCT should estimate when <tt>nosamps</tt>>''N'': |

− | * '''1''' = STDSLCT | + | * '''1''' = STDSLCT selects ''N''-1, or |

* '''2''' = STDSLCT selects ''N'' {default}. | * '''2''' = STDSLCT selects ''N'' {default}. | ||

− | Output isel is a vector of length nosamps containing the indices of the selected samples. | + | Output <tt>isel</tt> is a vector of length <tt>nosamps</tt> containing the indices of the selected samples. |

This routine is used to initialize the selection of samples in the DOPTIMAL function. Altough it does not satisfy the d-optimality condition, it is an alternative to doptimal that does not require an inverse or calculation of a determinant. | This routine is used to initialize the selection of samples in the DOPTIMAL function. Altough it does not satisfy the d-optimality condition, it is an alternative to doptimal that does not require an inverse or calculation of a determinant. | ||

+ | |||

+ | ====Inputs==== | ||

+ | |||

+ | * '''x''': data set, ''M'' by ''N'' | ||

+ | * '''nosamps''': number of selected samples | ||

+ | |||

+ | ====Optional Inputs==== | ||

+ | |||

+ | * '''flag''': how many samples to select when <tt>nosamps</tt>>''N''; a value of 1 selects ''N''-1, while a value of 2 (default) selects ''N''. | ||

+ | |||

+ | ====Outputs==== | ||

+ | |||

+ | * '''isel''': vector containing the indices of the selected samples | ||

===See Also=== | ===See Also=== | ||

[[doptimal]], [[stdsslct]] | [[doptimal]], [[stdsslct]] |

## Revision as of 16:21, 8 October 2008

### Purpose

Select samples on the exterior of a data space based on a Euclidean distance.

### Synopsis

- isel = distslct(x,nosamps,
*flag*)

### Description

DISTSLCT first identifies a sample in the *M* by *N* data set x furthest from the data set mean. Subsequent samples are selected to be simultaneously the furthest from the mean and the selected samples for a total of `nosamps` selected samples. DISTSLCT calls STDSSLCT to find the number of samples up to the rank of the data and uses a distance measure to find additional samples if `nosamps>rank(x)`.

Optional intput tells DISTSLCT how many samples STDSLCT should estimate when `nosamps`>*N*:

**1**= STDSLCT selects*N*-1, or

**2**= STDSLCT selects*N*{default}.

Output `isel` is a vector of length `nosamps` containing the indices of the selected samples.

This routine is used to initialize the selection of samples in the DOPTIMAL function. Altough it does not satisfy the d-optimality condition, it is an alternative to doptimal that does not require an inverse or calculation of a determinant.

#### Inputs

**x**: data set,*M*by*N***nosamps**: number of selected samples

#### Optional Inputs

**flag**: how many samples to select when`nosamps`>*N*; a value of 1 selects*N*-1, while a value of 2 (default) selects*N*.

#### Outputs

**isel**: vector containing the indices of the selected samples