View source: R/hypervolume_overlap_confidence.R

hypervolume_overlap_confidence | R Documentation |

Generates confidence intervals of four different overlap statistics. In order to find the confidence interval for the overlap statistics of two hypervolumes, use `hypervolume_resample`

twice to generate bootstraps. The function takes in paths to two sets of bootstrapped hypervolumes and gets overlap statistics for each possible pair. Confidence interval is calculated by taking a quantile of generated overlap statistics.

```
hypervolume_overlap_confidence(path1, path2, CI = .95, cores = 1)
```

`path1` |
A path to a directory of bootstrapped hypervolumes |

`path2` |
A path to a directory of bootstrapped hypervolumes |

`CI` |
Desired confidence interval proportion |

`cores` |
Number of logical cores to use while generating overlap statistics. If parallel backend already registered to |

The four overlap statistics are Sorensen, Jaccard, frac_unique_1, frac_unique_2. See `hypervolume_overlap_statistics`

Each hypervolume from path1 is overlapped with each hypervolume from path2 using `hypervolume_set`

. The four overlap statistics are calculated for each overlap.

`jaccard` |
Confidence interval for jaccard similarity score |

`sorensen` |
Confidence interval for sorensen similarity score |

`frac_unique_1` |
Confidence interval for fraction of first hypervolume that is unique |

`frac_unique_2` |
Confidence interval for fraction of second hypervolume that is unique |

`distribution` |
a matrix of overlap statistics used to generate the confidence intervals |

`hypervolume_resample`

```
## Not run:
# Let us overlap two hypervolumes generated from multivariate nomral
# distributions with different means and same covariance matrices.
sample1 = rmvnorm(150, mean = c(0, 0))
sample2 = rmvnorm(150, mean = c(0.5, 0.5))
hv1 = hypervolume(sample1)
hv2 = hypervolume(sample2)
# generates confidence intervals from quantiles of 20*20 overlaps
path1 = hypervolume_resample("mean_0_0", hv1, n = 20)
path2 = hypervolume_resample("mean_0.5_0.5", hv2, n = 20)
result = hypervolume_overlap_confidence(path1, path2)
# confidence index of Sorensen coefficient
print(result["sorensen"])
## End(Not run)
```

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