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Rather than a single matrix or data frame, it would save space to store persistence data as a list of 2-column matrices. Dimension would be inferred from position in the list, and metadata (e.g. extended persistence) could be stored in a separate list with same-dimension members. This assumes that TDA uses only nonnegative integer dimensions, which is unlikely to change any time soon.
With the above chance, a coercion method as.data.frame() would recover the current form of output (without the 'PHom' class).
Parameters like the maximum dimension and the distance threshold should be retained in the output object, as list elements or (less likely) as attributes. These are important for interpretation and may be used in downstream steps like plotting or calculating landscapes.
Helper functions could be written to access birth–death pairs for a single dimension. These would be useful in analysis pipelines.
The text was updated successfully, but these errors were encountered:
While experimenting with Jose Bouza's tda-tools package, some possible improvements suggest themselves:
as.data.frame()
would recover the current form of output (without the 'PHom' class).The text was updated successfully, but these errors were encountered: