incline.estimate

incline.estimate(smoother, df, value_column='value', time_column=None, derivative_order=1, with_uncertainty=False, noise=None, bias_correct=False, simultaneous=False, confidence_level=0.95, pilot_scale=None, n_bootstrap=200, random_state=None)[source]

Run a smoother over a DataFrame and return the structured estimate.

Parameters:

  • smoother (Smoother) – The smoother to fit.

  • df (pd.DataFrame) – Time series data.

  • value_column (str) – Column holding the values.

  • time_column (str | None) – Numeric time column. The index is used when None.

  • derivative_order (int) – Which derivative to estimate.

  • with_uncertainty (bool) – Whether to compute standard errors.

  • noise (NoiseModel | str | None) – Noise model instance, or 'iid', 'ar1', or 'heteroskedastic'. Used for uncertainty; an adaptive smoothing spline also uses it for the point fit. Rejected when the selected route cannot use it.

  • bias_correct (bool) – Subtract estimated smoothing bias. Linear smoothers only.

  • simultaneous (bool) – Return a whole-curve band for a fixed linear smoother.

  • confidence_level (float) – Confidence level for intervals.

  • pilot_scale (float | None) – Scale of the pilot fit used for bias correction.

  • n_bootstrap (int) – Replicates for nonlinear smoothers.

  • random_state (int | np.random.Generator | None) – Seed or Generator.

Returns:

The estimate, which .to_frame(df) renders as a DataFrame.

Return type:

TrendEstimate