Description Usage Arguments Value Author(s) References Examples

Given a sample x1,...,x_n, it is evaluated the distribution of the Hausdorff distances between xi + B(m,r) and xj + B(m,r) where: xi and xj are two different points from the sample; m is the sample mean of the xi's; r is a positive value and B(m,r) is the disc centered at m with radius r. The i-th point xi has probability prob[i].

1 | ```
exactHausdorff(A, prob, r)
``` |

`A` |
A matrix where each row corresponds with a different point. |

`prob` |
The probabilities of each row of A. If we are dealing with the empirical distribution then all points are equiprobable and prob = rep(1/nrow(A),nrow(A) |

`r` |
A positive number. |

`distance` |
The observed distances between xi + B(m,r) and xj + B(m,r) where: xi and xj are two different points from the sample. |

`probability` |
Probabilities of each distance. |

`alldistances` |
The whole set of distances with repetitions. |

Guillermo Ayala <[email protected]>

Miguel Lopez-Diaz. An indexed multivariate dispersion ordering based on the Hausdorff distance. Journal of Multivariate Analysis, 97(7):1623 - 1637, 2006.

G. Ayala, M.C. Lopez-Diaz, M. Lopez-Diaz and L. Martinez-Costa. Methods and algorithms to test the simplex and Hausdorff dispersion orders with a simulation study and an ophthalmological application. Technical Report. 2012

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
library(disp2D)
library(geometry)
library(mvtnorm)
sigma1 = matrix(c(0.912897,1.092679,1.092679,1.336440),byrow=TRUE,ncol=2)
sigma2 = sigma1 + diag(1,ncol=2,nrow=2)
A = rmvnorm(200,mean=rep(0,2),sigma=sigma1)
B = rmvnorm(200,mean=rep(0,2),sigma=sigma2)
r=.1
prob = probA = probB = rep(1/200,200)
HA = exactHausdorff(A,probA,r)
HB = exactHausdorff(B,probB,r)
plot(HA$distance, cumsum(HA$probability), type = "l", xlab = "",
ylab = "DF", xlim = range(c(HA,HB)))
lines(HB$distance, cumsum(HB$probability), lty = 2)
``` |

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