## summaries.glance()


Return a one-row model-summary DataFrame for a fitted model.


Usage

``` python
summaries.glance(
    model,
    **kwargs,
)
```


Produces a single row of model-level statistics such as the number of observations, number of events, log-likelihood, AIC, and concordance. This is the Python equivalent of R's `broom::glance()`.


## Parameters


`model: object`  
A fitted Greenwood estimator (e.g., [CoxPH](CoxPH.md#greenwood.CoxPH), [AFT](AFT.md#greenwood.AFT), [Parametric](Parametric.md#greenwood.Parametric)).

`**kwargs: Any`  
Forwarded to the registered adapter. Common options include `format=`.


## Returns


`DataFrame`  
A one-row model-level summary.


## Examples

Glance at a fitted Cox model for its overall fit statistics:


``` python
import greenwood as gw

# Load data, build a right-censored response, and fit a Cox model
lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
cox = gw.CoxPH().fit(y, covariates=lung[["age", "sex"]])

# Glance at overall model-fit statistics
gw.glance(cox, format="polars")
```


shape: (1, 10)

| n | nevent | loglik | aic | lr_statistic | df | lr_p_value | frailty_theta | frailty_lrt_statistic | frailty_lrt_p_value |
|----|----|----|----|----|----|----|----|----|----|
| i64 | i64 | f64 | f64 | f64 | i64 | f64 | null | null | null |
| 228 | 165 | -742.848246 | 1489.696492 | 14.123111 | 2 | 0.000857 | null | null | null |