Description Usage Arguments Details Value References See Also Examples

`tauAP`

is the AP rank correlation coefficient by Yilmaz et al., where neither vector can
contain tied items. `tauAP_a`

and `tauAP_b`

are the versions developed by Urbano and
Marrero to cope with ties under the scenarios of accuracy and agreement, respectively. See the
references for details.

1 2 3 4 5 |

`x` |
a numeric vector. In |

`y` |
a numeric vector of the same length as |

`decreasing` |
logical. Should the sort order be increasing or decreasing (default)? |

Note that the sorting order is decreasing by default, as should be for instance if the scores
represent the effectiveness of systems. When the sorting order is ascending, as is for instance when the vectors represent ranks, the parameter
`decreasing`

must be set to `FALSE`

.

The correlation coefficient.

E. Yilmaz, J.A. Aslam and S. Robertson (2008). A New Rank Correlation Coefficient for Information Retrieval. ACM SIGIR.

J. Urbano and M. Marrero (2017). The Treatment of Ties in AP Correlation. ACM ICTIR.

`tau`

for Kendall correlation coefficients.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# No ties
x <- c(0.67, 0.45, 0.29, 0.12, 0.57, 0.24, 0.94, 0.75, 0.08, 0.54)
y <- c(0.48, 0.68, 0.32, 0.09, 0.06, 0.61, 0.87, 0.22, 0.44, 0.84)
tauAP(x, y)
tauAP_a(x,y) # same as tauAP
# Ties in y
y <- round(y, 1)
tauAP_a(x, y)
tauAP_b(x, y)
# Ties in x too
x <- round(x, 1)
tauAP_b(x, y)
# Set decreasing to FALSE when x and y already represent ranks
x <- rank(-x)
y <- rank(-y)
tauAP_b(x, y, FALSE) # same as above
``` |

```
[1] 0.3928571
[1] 0.3928571
[1] 0.39903
[1] 0.3317019
[1] 0.339418
[1] 0.339418
```

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