Description Usage Arguments Value References Examples
Calculates sequential Jacobians on time series data using the S-map method developed and detailed in the 2016 Proceedings of the Royal Society B paper titled Tracking and Forecasting Ecological Interactions in Real Time by Deyle, May, Munch, and Sugihara.
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time_series |
A data frame where each column is a time series. |
target |
Which time series (column) to calculate the sequential interactions of. |
dates |
Optional vector of dates. |
theta |
Parameter to tune the relationship between distance and weight in the linear model. Defaults to 8 which was used by Deyle, May, Munch, and Sugihara. |
manual_block |
Defaults to FALSE. If your data is stacked spatial replicates, where you have already made the first column for time 1+1 and all others for time t. |
causal_probabilities |
Causal probabilities, or 1 - p-values from a test for causality, for each interactor (column in the data). |
causal_iterations |
Defaults to 100. Determines how many times the causal filter is applied. For large datasets this can cause slowdowns which is why the default is only 100. |
A data frame where each column is a row of the sequential Jacobian.
Deyle, E. R., May, R. M., Munch, S. B., & Sugihara, G. (2016, January). Tracking and forecasting ecosystem interactions in real time. In Proc. R. Soc. B (Vol. 283, No. 1822, p. 20152258). The Royal Society.
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