GeneralizedGammaRegressionFitter

class lifelines.fitters.generalized_gamma_regression_fitter.GeneralizedGammaRegressionFitter(alpha: float = 0.05, penalizer: float | ndarray = 0.0, l1_ratio: float = 0.0, **kwargs)

This class implements a Generalized Gamma model for regression data. The model has parameterized form:

The survival function is:

\[\begin{split}S(t; x)=\left\{ \begin{array}{} 1-\Gamma_{RL}\left( \frac{1}{{{\lambda }^{2}}};\frac{{e}^{\lambda \left( \frac{\log(t)-\mu }{\sigma} \right)}}{\lambda ^{2}} \right) \textit{ if } \lambda> 0 \\ \Gamma_{RL}\left( \frac{1}{{{\lambda }^{2}}};\frac{{e}^{\lambda \left( \frac{\log(t)-\mu }{\sigma} \right)}}{\lambda ^{2}} \right) \textit{ if } \lambda \le 0 \\ \end{array} \right.\,\!\end{split}\]

where \(\Gamma_{RL}\) is the regularized lower incomplete Gamma function, and \(\sigma = \sigma(x) = \exp(\alpha x^T), \lambda = \lambda(x) = \beta x^T, \mu = \mu(x) = \gamma x^T\).

This model has the Exponential, Weibull, Gamma and Log-Normal as sub-models, and thus can be used as a way to test which model to use:

1. When \(\lambda = 1\) and \(\sigma = 1\), then the data is Exponential.

2. When \(\lambda = 1\) then the data is Weibull.

3. When \(\sigma = \lambda\) then the data is Gamma.

4. When \(\lambda = 0\) then the data is Log-Normal.

5. When \(\lambda = -1\) then the data is Inverse-Weibull.

6. When \(-\sigma = \lambda\) then the data is Inverse-Gamma.

After calling the .fit method, you have access to properties like: cumulative_hazard_, survival_function_, A summary of the fit is available with the method print_summary().

Important

The parameterization implemented has \(\log\sigma\), thus there is a ln_sigma_ in the output. Exponentiate this parameter to recover \(\sigma\).

Important

This model is experimental. It’s API may change in the future. Also, it’s convergence is not very stable.

Parameters:

• alpha (float, optional (default=0.05)) – the level in the confidence intervals.

• penalizer (float or array, optional (default=0.0)) – the penalizer coefficient to the size of the coefficients. See l1_ratio. Must be equal to or greater than 0. Alternatively, penalizer is an array equal in size to the number of parameters, with penalty coefficients for specific variables. For example, penalizer=0.01 * np.ones(p) is the same as penalizer=0.01

Examples

from lifelines import GeneralizedGammaFitter
from lifelines.datasets import load_waltons
waltons = load_waltons()

ggf = GeneralizedGammaFitter()
ggf.fit(waltons['T'], waltons['E'])
ggf.plot()
ggf.summary

cumulative_hazard_

The estimated cumulative hazard (with custom timeline if provided)

Type:

DataFrame

hazard_

The estimated hazard (with custom timeline if provided)

Type:

DataFrame

survival_function_

The estimated survival function (with custom timeline if provided)

Type:

DataFrame

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

variance_matrix_

The variance matrix of the coefficients

Type:

DataFrame

median_

The median time to event

Type:

float

lambda_

The fitted parameter in the model

Type:

float

rho_

The fitted parameter in the model

Type:

float

alpha_

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