predict_aj_estimates()

Evaluate state-occupancy probabilities at fixed time horizons or scheduled steps.

Usage

Source

predict_aj_estimates(
    event_table, fixed_time_horizons=None, full_event_table=False
)

Parameters

event_table: pl.DataFrame or TransitionTableSteps

Event table returned by prepare_event_table or transition table returned by prepare_transition_table_steps.

fixed_time_horizons: pl.Series = None

Numeric horizons to evaluate for time-based event tables, or integer step horizons for step transition tables. If omitted for step transition tables, occupancy for all observed steps is returned.

full_event_table: bool = False

If True, append estimates at every observed event time. Use the estimate_origin column to distinguish those rows from requested horizons. Only supported for continuous event tables.

Returns

pl.DataFrame

For competing risk event tables: columns are times, state-occupancy probabilities for states 0, 1, and 2, and estimate_origin. For discrete step transition tables: columns are step, state, and state_occupancy_probability.

Raises

TypeError

If inputs are not the documented Polars/polarstate types, horizons are not of expected numeric/integer dtypes, or full_event_table is not boolean.

ValueError

If required event-table columns are missing, either input is empty, or horizons contain null, non-finite, invalid, or unobserved step values.