Description Arguments Details Value Author(s) References

View source: R/windowed_sindy.R

Run SINDy on raw data with a sliding window approach

`xs` |
Matrix of raw data |

`dx` |
Matrix of main system variable dervatives; if NULL, it estimates with finite differences from xs |

`dt` |
Sample interval, if data continuously sampled; default = 1 |

`Theta` |
Matrix of features; if not supplied, assumes polynomial features of order 3 |

`lambda` |
Threshold to use for iterated least squares sparsification (Brunton et al.) |

`fit.its` |
Number of iterations to conduct the least-square threshold sparsification; default = 10 |

`B.expected` |
The function will compute a goodness of fit if supplied with an expected coefficient matrix B; default = NULL |

`window.size` |
Size of window to segment raw data as separate time series; defaults to deciles |

`window.shift` |
Step sizes across windows, permitting overlap; defaults to deciles |

A convenience function for extracting a list of coefficients on segments of a time series. This facilitates using SINDy output as source of descriptive measures of dynamics.

It returns a list of coefficients Bs containing B coefficients at each window

Rick Dale and Harish S. Bhat

Dale, R. and Bhat, H. S. (in press). Equations of mind: data science for inferring nonlinear dynamics of socio-cognitive systems. Cognitive Systems Research.

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