PanelRegression.plot_trajectories#

PanelRegression.plot_trajectories(units=None, n_sample=10, select='random', show_mean=True, hdi_prob=0.94, interval_type='mean')[source]#

Plot unit-level time series trajectories.

Shows actual vs fitted values for selected units over time. Useful for visualizing within-unit model fit and identifying problematic units.

Parameters:

  • units (list[str] | None) – Specific unit IDs to plot. If provided, ignores n_sample and select.

  • n_sample (int) – Number of units to sample if units not specified.

  • select (Literal['random', 'extreme', 'high_variance']) –

    Method for selecting units:

    • ”random”: Random sample of units

    • ”extreme”: Units with largest positive and negative effects

    • ”high_variance”: Units with most within-unit variation

  • show_mean (bool) – Whether to show the overall mean trajectory.

  • hdi_prob (float) – Probability mass for the HDI credible interval (Bayesian models only). Defaults to HDI_PROB (currently 0.94). Common alternative values are 0.89 or 0.5.

  • interval_type (Literal['mean', 'predictive']) –

    Which uncertainty interval to show for Bayesian models:

    • ”mean”: HDI of posterior mu (uncertainty in expected value)

    • ”predictive”: HDI of posterior predictive y_hat (includes observation noise)

Returns:

Figure and array of axes objects

Return type:

tuple[plt.Figure, np.ndarray]

Raises:

ValueError – If time_fe_variable is not provided (cannot plot trajectories without time)