mindist {DiceDesign} | R Documentation |

## Mindist measure

### Description

Compute the `mindist`

criterion (also called maximin)

### Usage

`mindist(design)`

### Arguments

`design` |
a matrix (or a data.frame) representing the design of experiments in the unit cube [0,1] |

### Details

The mindist criterion is defined by

`mindist= \min_{x_{i}\in X} \left( \gamma_{i} \right)`

where `\gamma_{i}`

is the minimal distance between the point `x_{i}`

and the other points `x_{k}`

of the `design`

.

A higher value corresponds to a more regular scaterring of design points.

### Value

A real number equal to the value of the mindist criterion for the `design`

.

### Author(s)

J. Franco

### References

Gunzburer M., Burkdart J. (2004), Uniformity measures for point samples in hypercubes, https://people.sc.fsu.edu/~jburkardt/.

Jonshon M.E., Moore L.M. and Ylvisaker D. (1990), Minmax and maximin distance designs, *J. of Statis. Planning and Inference*, 26, 131-148.

Chen V.C.P., Tsui K.L., Barton R.R. and Allen J.K. (2003), A review of design and modeling in computer experiments, *Handbook of Statistics*, 22, 231-261.

### See Also

other distance criteria like `meshRatio`

and `phiP`

,
discrepancy measures provided by `discrepancyCriteria`

.

### Examples

```
dimension <- 2
n <- 40
X <- matrix(runif(n*dimension), n, dimension)
mindist(X)
```

*DiceDesign*version 1.10 Index]