# Distslct

### 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