## summaries.augment()


Return an observation-level DataFrame for a fitted model.


Usage

``` python
summaries.augment(
    model,
    data=None,
    **kwargs,
)
```


Produces one row per observation, appending model-derived columns (e.g., fitted values, residuals, or predicted survival probabilities) to the original data. This is the Python equivalent of R's `broom::augment()`.


## Parameters


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

`data: Any = None`  
The original data used to fit the model. Required by some adapters (e.g., Cox residuals need the covariate matrix); optional for others.

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


## Returns


`DataFrame`  
An observation-level summary with predictions or residuals.


## Examples

Once an augment adapter is registered for a model class, call [augment()](augment.md#greenwood.augment) to get observation-level predictions or residuals:

``` python
import greenwood as gw

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"]])
gw.augment(cox, data=lung, format="polars")
```