ExponentialFitter¶
- class lifelines.fitters.exponential_fitter.ExponentialFitter(*args, **kwargs)¶
This class implements an Exponential model for univariate data. The model has parameterized form:
\[S(t) = \exp\left(\frac{-t}{\lambda}\right), \lambda >0\]which implies the cumulative hazard rate is
\[H(t) = \frac{t}{\lambda}\]and the hazard rate is:
\[h(t) = \frac{1}{\lambda}\]After calling the
.fit
method, you have access to properties like:survival_function_
,lambda_
,cumulative_hazard_
A summary of the fit is available with the methodprint_summary()
- Parameters:
alpha (float, optional (default=0.05)) – the level in the confidence intervals.
- cumulative_hazard_¶
The estimated cumulative hazard (with custom timeline if provided)
- Type:
DataFrame
- confidence_interval_cumulative_hazard_¶
The lower and upper confidence intervals for the cumulative hazard
- Type:
DataFrame
- hazard_¶
The estimated hazard (with custom timeline if provided)
- Type:
DataFrame
- confidence_interval_hazard_¶
The lower and upper confidence intervals for the hazard
- Type:
DataFrame
- survival_function_¶
The estimated survival function (with custom timeline if provided)
- Type:
DataFrame
- confidence_interval_survival_function_¶
The lower and upper confidence intervals for the survival function
- Type:
DataFrame
- variance_matrix_¶
The variance matrix of the coefficients
- Type:
DataFrame
- median_survival_time_¶
The median time to event
- Type:
float
- lambda_¶
The fitted parameter in the model
- Type:
float
- durations¶
The durations provided
- Type:
array
- event_observed¶
The event_observed variable provided
- Type:
array
- timeline¶
The time line to use for plotting and indexing
- Type:
array
- entry¶
The entry array provided, or None
- Type:
array or None
- cumulative_density_¶
The estimated cumulative density function (with custom timeline if provided)
- Type:
DataFrame
- density_¶
The estimated density function (PDF) (with custom timeline if provided)
- Type:
DataFrame
- confidence_interval_cumulative_density_¶
The lower and upper confidence intervals for the cumulative density
- Type:
DataFrame
- percentile(p)¶
Return the unique time point, t, such that S(t) = p.
- Parameters:
p (float)