statsmodels.duration.hazard_regression.PHReg

class statsmodels.duration.hazard_regression.PHReg(endog, exog, status=None, entry=None, strata=None, offset=None, ties='breslow', missing='drop', **kwargs)

Fit the Cox proportional hazards regression model for right censored data.

Parameters:

endog : array-like

The observed times (event or censoring)

exog : 2D array-like

The covariates or exogeneous variables

status : array-like

The censoring status values; status=1 indicates that an event occured (e.g. failure or death), status=0 indicates that the observation was right censored. If None, defaults to status=1 for all cases.

entry : array-like

The entry times, if left truncation occurs

strata : array-like

Stratum labels. If None, all observations are taken to be in a single stratum.

ties : string

The method used to handle tied times, must be either ‘breslow’ or ‘efron’.

offset : array-like

Array of offset values

missing : string

The method used to handle missing data

Notes

Proportional hazards regression models should not include an explicit or implicit intercept. The effect of an intercept is not identified using the partial likelihood approach.

endog, event, strata, entry, and the first dimension of exog all must have the same length

Attributes

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables

Methods

baseline_cumulative_hazard(params)	Estimate the baseline cumulative hazard and survival functions.
baseline_cumulative_hazard_function(params)	Returns a function that calculates the baseline cumulative hazard function for each stratum.
breslow_gradient(params)	Returns the gradient of the log partial likelihood, using the Breslow method to handle tied times.
breslow_hessian(params)	Returns the Hessian of the log partial likelihood evaluated at params, using the Breslow method to handle tied times.
breslow_loglike(params)	Returns the value of the log partial likelihood function evaluated at params, using the Breslow method to handle tied times.
efron_gradient(params)	Returns the gradient of the log partial likelihood evaluated at params, using the Efron method to handle tied times.
efron_hessian(params)	Returns the Hessian matrix of the partial log-likelihood evaluated at params, using the Efron method to handle tied times.
efron_loglike(params)	Returns the value of the log partial likelihood function evaluated at params, using the Efron method to handle tied times.
fit([groups])	Fit a proportional hazards regression model.
fit_regularized([method, alpha, ...])	Return a regularized fit to a linear regression model.
from_formula(formula, data[, status, entry, ...])	Create a proportional hazards regression model from a formula and dataframe.
get_distribution(params)	Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case.
hessian(params)	Returns the Hessian matrix of the log partial likelihood function evaluated at params.
information(params)	Fisher information matrix of model
initialize()	Initialize (possibly re-initialize) a Model instance.
loglike(params)	Returns the log partial likelihood function evaluated at params.
predict(params[, exog, cov_params, endog, ...])	Returns predicted values from the proportional hazards regression model.
robust_covariance(params)	Returns a covariance matrix for the proportional hazards model regresion coefficient estimates that is robust to certain forms of model misspecification.
score(params)	Returns the score function evaluated at params.
score_residuals(params)	Returns the score residuals calculated at a given vector of parameters.
weighted_covariate_averages(params)	Returns the hazard-weighted average of covariate values for subjects who are at-risk at a particular time.

Methods

baseline_cumulative_hazard(params)	Estimate the baseline cumulative hazard and survival functions.
baseline_cumulative_hazard_function(params)	Returns a function that calculates the baseline cumulative hazard function for each stratum.
breslow_gradient(params)	Returns the gradient of the log partial likelihood, using the Breslow method to handle tied times.
breslow_hessian(params)	Returns the Hessian of the log partial likelihood evaluated at params, using the Breslow method to handle tied times.
breslow_loglike(params)	Returns the value of the log partial likelihood function evaluated at params, using the Breslow method to handle tied times.
efron_gradient(params)	Returns the gradient of the log partial likelihood evaluated at params, using the Efron method to handle tied times.
efron_hessian(params)	Returns the Hessian matrix of the partial log-likelihood evaluated at params, using the Efron method to handle tied times.
efron_loglike(params)	Returns the value of the log partial likelihood function evaluated at params, using the Efron method to handle tied times.
fit([groups])	Fit a proportional hazards regression model.
fit_regularized([method, alpha, ...])	Return a regularized fit to a linear regression model.
from_formula(formula, data[, status, entry, ...])	Create a proportional hazards regression model from a formula and dataframe.
get_distribution(params)	Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case.
hessian(params)	Returns the Hessian matrix of the log partial likelihood function evaluated at params.
information(params)	Fisher information matrix of model
initialize()	Initialize (possibly re-initialize) a Model instance.
loglike(params)	Returns the log partial likelihood function evaluated at params.
predict(params[, exog, cov_params, endog, ...])	Returns predicted values from the proportional hazards regression model.
robust_covariance(params)	Returns a covariance matrix for the proportional hazards model regresion coefficient estimates that is robust to certain forms of model misspecification.
score(params)	Returns the score function evaluated at params.
score_residuals(params)	Returns the score residuals calculated at a given vector of parameters.
weighted_covariate_averages(params)	Returns the hazard-weighted average of covariate values for subjects who are at-risk at a particular time.

Attributes

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables