`R/vietoris_rips.R`

`vietoris_rips.Rd`

This function is an R wrapper for the Ripser C++ library to calculate
persistent homology. For more information on the C++ library, see
https://github.com/Ripser/ripser. For more information on how objects of
different classes are evaluated by `vietoris_rips`

, read the Details section
below.

vietoris_rips(dataset, ...) # S3 method for data.frame vietoris_rips(dataset, ...) # S3 method for matrix vietoris_rips(dataset, max_dim = 1L, threshold = -1, p = 2L, ...) # S3 method for dist vietoris_rips(dataset, max_dim = 1L, threshold = -1, p = 2L, ...) # S3 method for numeric vietoris_rips( dataset, data_dim = 2L, dim_lag = 1L, sample_lag = 1L, method = "qa", ... ) # S3 method for ts vietoris_rips(dataset, ...) # S3 method for default vietoris_rips(dataset, ...)

dataset | object on which to calculate persistent homology |
---|---|

... | other relevant parameters |

max_dim | maximum dimension of persistent homology features to be calculated |

threshold | maximum simplicial complex diameter to explore |

p | prime field in which to calculate persistent homology |

data_dim | desired end data dimension |

dim_lag | time series lag factor between dimensions |

sample_lag | time series lag factor between samples (rows) |

method | currently only allows |

`PHom`

object

`vietoris_rips.data.frame`

assumes `dataset`

is a point cloud, with each row
representing a point and each column representing a dimension.

`vietoris_rips.matrix`

currently assumes `dataset`

is a point cloud (similar
to `vietoris_rips.data.frame`

). Currently in the process of adding network
representation to this method.

`vietoris_rips.dist`

takes a `dist`

object and calculates persistent homology
based on pairwise distances. The `dist`

object could have been calculated
from a point cloud, network, or any object containing elements from a finite
metric space.

`vietoris_rips.numeric`

and `vietoris_rips.ts`

both calculate persistent
homology of a time series object. The time series object is converted to a
matrix using the quasi-attractor method detailed in Umeda (2017)
doi:10.1527/tjsai.D-G72. Persistent homology of the resulting matrix is
then calculated.