# SmoothHRResult


Smooth non-linear hazard ratio curve for a continuous covariate.


Usage

``` python
SmoothHRResult(
    term,
    reference,
    grid,
    log_hr,
    hr,
    log_hr_lower,
    log_hr_upper,
    hr_lower,
    hr_upper,
    df,
    knots,
    adjustment,
)
```


Contains the estimated log-hazard ratio (and hazard ratio) as a function of a continuous covariate, computed by refitting the Cox model with a B-spline basis expansion. The curve shows how the covariate's effect on the hazard varies across its range, relative to a reference value.


## Attributes


`term: str`  
Name of the covariate whose smooth effect is estimated.

`reference: float`  
Reference value of the covariate. The log-HR is zero at this point.

`grid: Array`  
Array of covariate values at which the curve is evaluated.

`log_hr: Array`  
Log hazard ratio at each grid point (relative to `reference`).

`hr: Array`  
Hazard ratio at each grid point (`exp(log_hr)`).

`log_hr_lower: Array`  
Lower confidence bound on the log-HR.

`log_hr_upper: Array`  
Upper confidence bound on the log-HR.

`hr_lower: Array`  
Lower confidence bound on the HR (`exp(log_hr_lower)`).

`hr_upper: Array`  
Upper confidence bound on the HR (`exp(log_hr_upper)`).

`df: int`  
Degrees of freedom of the spline (number of basis functions).

`knots: Array`  
Interior knot positions used for the B-spline basis.

`adjustment: dict[str, float]`  
Dictionary of covariate names and the values they were held at.


## Methods

| Name | Description |
|----|----|
| [to_frame()](#to_frame) | Return the smooth curve as a DataFrame. |

------------------------------------------------------------------------


### to_frame()


Return the smooth curve as a DataFrame.


Usage

``` python
to_frame(
    *,
    scale="log_hr",
    format=None,
)
```


#### Parameters


`scale: str = ``"log_hr"`  
`"log_hr"` (default) returns log hazard ratios and confidence bounds. `"hr"` returns hazard ratios (exponentiated).

`format: str | None = None`  
Output format: `None` (default), `"pandas"`, `"polars"`, or `"pyarrow"`.


#### Returns


`pandas.DataFrame, polars.DataFrame, or pyarrow.Table`