njab.plotting package#
Matplotlib functionality for custom plots.
- njab.plotting.compare_km_curves(time: Series, y: Series, pred: Series, ax: Axes | None = None, ylim: tuple[int] = (0, 1), xlim: tuple[int] = (0, 180), xlabel: str | None = None, ylabel: str | None = None, add_risk_counts=False) Axes[source]#
Compare Kaplan-Meier curves for two groups (e.g. based on binary prediction)
- Parameters:
time (pd.Series) – Time to event variable
y (pd.Series) – event variable
pred (pd.Series) – mask for two groups, e.g. predictions
ax (Axes, optional) – matplotlib Axes object, by default None
ylim (tuple[int], optional) – y-axis bounds, by default (0, 1)
xlim (tuple[int], optional) – time-axis bounds, by default (0, 730)
xlabel (str, optional) – time-axis label, by default None
ylabel (str, optional) – y-axis label, by default None
- Returns:
Axes object, KaplanMeierFitter for predited as 0, KaplanMeierFitter for predicted as 1
- Return type:
Axes, KaplanMeierFitter, KaplanMeierFitter
- njab.plotting.plot_lifelines(obs: DataFrame, ax=None, start_col='DateDiagnose', end_col='DateDeath', status_col='dead') Axes[source]#
Plot a lifeline for each observation.