Within our evaluation study, we compared 45 methods on four aspects:
Perhaps not surprisingly, we found a high diversity in method performance, and that not many methods perform well across the board. The performance of a method depended on many factors, mainly the dimensions of the data and the kind of trajectory present in the data. Based on this, we developed an interactive shiny app which you can use to explore the results and select an optimal set of methods for your analysis.
This app can be opened using
dynguidelines::guidelines_shiny(). It is recommended to give this function your dataset, so that it will precalculate some fields for you:
dataset <- example_dataset guidelines_shiny(dataset = dataset)
The app includes a tutorial, which will guide you through the user interface. Once finished, it is highly recommended to copy over the code that generates the guidelines to your script, so that your analysis remains reproducible, for example:
dataset <- example_dataset guidelines <- guidelines( dataset, answers = answer_questions( dataset, multiple_disconnected = FALSE, expect_topology = TRUE, expected_topology = "linear" ) ) ## Loading required namespace: akima
This guidelines object contains: