Description Usage Arguments Examples

An empirical method (bootstrap) to differentiate between features that constitute signal versus noise based on the magnitude of their persistence relative to one another. Note: you must have at least 5 features of a given dimension to use this function.

1 | ```
id_significant(features, dim = 1, reps = 100, cutoff = 0.975)
``` |

`features` |
3xn data frame of features; the first column must be dimension, the second birth, and the third death |

`dim` |
dimension of features of interest |

`reps` |
number of replicates |

`cutoff` |
percentile cutoff past which features are considered significant |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# get dataset (noisy circle) and calculate persistent homology
angles <- runif(100, 0, 2 * pi)
x <- cos(angles) + rnorm(100, mean = 0, sd = 0.1)
y <- sin(angles) + rnorm(100, mean = 0, sd = 0.1)
annulus <- cbind(x, y)
phom <- calculate_homology(annulus)
# find threshold of significance
# expecting 1 significant feature of dimension 1 (Betti-1 = 1 for annulus)
thresh <- id_significant(features = as.data.frame(phom),
dim = 1,
reps = 500,
cutoff = 0.975)
# generate flat persistence diagram
# every feature higher than `thresh` is significant
plot_persist(phom, flat = TRUE)
``` |

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