Description Usage Arguments Details Value Author(s) See Also Examples

These functions compute Absolute (`ab.dist`

), Euclidean
(`eu.dist`

) or Mahalanobis (`mh.dist`

) distances between two
points. The variant functions (`*.matY`

), accomplish the same task,
but between a point on the one hand, and every point specified as rows of a matrix on the other.

1 2 3 4 5 6 | ```
ab.dist(x, y)
eu.dist(x, y)
mh.dist(x, y, A)
ab.dist.matY(x, Y)
eu.dist.matY(x, Y)
mh.dist.matY(x, Y, A)
``` |

`x` |
The vector (point) from which distance is sought. |

`y` |
The vector (point) to which distance is sought. |

`Y` |
A set of points, specified as rows of a matrix, to which distances are sought. |

`A` |
The inverse matrix to use for the Mahalanobis distance. |

These functions are used internally to decide how the nearest neighbours shall be calculated; the user need not call any of these functions directly. Rather, the choice of distance is specified as a string ('euclidean' or 'absolute' or 'mahal').

Either a single number for the distance, or a vector of distances,
corresponding to each row of `Y`

.

Mohit Dayal

get.NN

1 2 3 4 5 6 7 8 9 10 11 | ```
x <- c(1,2)
y <- c(0,3)
mu <- c(1,3)
Sigma <- rbind(c(1,0.2),c(0.2,1))
Y <- MASS::mvrnorm(20, mu = mu, Sigma = Sigma)
ab.dist(x,y)
eu.dist(x,y)
mh.dist(x,y,Sigma)
ab.dist.matY(x,Y)
eu.dist.matY(x,Y)
mh.dist.matY(x,Y,Sigma)
``` |

```
Warning message:
no DISPLAY variable so Tk is not available
[1] 2
[1] 2
[,1]
[1,] 2.5
[1] 2.2284123 2.2378047 1.1964977 0.9432708 1.8059748 2.3870570 1.6303574
[8] 2.5202570 0.7032441 2.7113114 1.7023114 2.7414863 2.1732236 1.5718124
[15] 2.3493425 3.6400791 3.2157438 1.9691935 1.5027929 1.9431484
[1] 2.7771813 3.4846290 0.9536751 0.4452661 1.9816146 3.2541393
[7] 1.4036025 3.2927857 0.2529855 3.9744493 1.4643519 4.1670429
[13] 2.3624025 1.8209725 2.8782691 11.6991019 5.3710730 1.9578152
[19] 1.4690891 2.6461325
[1] 3.3488639 3.9471429 0.8938424 0.3712160 1.7975297 2.8805821
[7] 1.7234324 4.0672579 0.2132002 3.4365597 1.2267182 5.0383164
[13] 2.9526064 2.0321842 3.5484347 11.8634240 4.5594646 2.4393717
[19] 1.6947381 2.5210353
```

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