statsmodels.duration.hazard_regression.PHRegResults

class statsmodels.duration.hazard_regression.PHRegResults(model, params, cov_params, scale=1.0, covariance_type='naive')

Class to contain results of fitting a Cox proportional hazards survival model.

PHregResults inherits from statsmodels.LikelihoodModelResults

Parameters:See statsmodels.LikelihoodModelResults

See also

statsmodels.LikelihoodModelResults

Attributes

normalized_cov_params()

See specific model class docstring

model	(class instance) PHreg model instance that called fit.
params	(ndarray) The coefficients of the fitted model. Each coefficient is the log hazard ratio corresponding to a 1 unit difference in a single covariate while holding the other covariates fixed.
bse	(ndarray) The standard errors of the fitted parameters.

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
get_distribution()	Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case.
initialize(model, params, **kwargs)	Initialize (possibly re-initialize) a Results instance.
load(fname)	Load a pickled results instance
normalized_cov_params()	See specific model class docstring
predict([endog, exog, strata, offset, …])	Returns predicted values from the proportional hazards regression model.
remove_data()	Remove data arrays, all nobs arrays from result and model.
save(fname[, remove_data])	Save a pickle of this instance.
summary([yname, xname, title, alpha])	Summarize the proportional hazards regression results.
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q.
t_test_pairwise(term_name[, method, alpha, …])	Perform pairwise t_test with multiple testing corrected p-values.
wald_test(r_matrix[, cov_p, scale, invcov, …])	Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, …])	Compute a sequence of Wald tests for terms over multiple columns.

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
get_distribution()	Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case.
initialize(model, params, **kwargs)	Initialize (possibly re-initialize) a Results instance.
load(fname)	Load a pickled results instance
normalized_cov_params()	See specific model class docstring
predict([endog, exog, strata, offset, …])	Returns predicted values from the proportional hazards regression model.
remove_data()	Remove data arrays, all nobs arrays from result and model.
save(fname[, remove_data])	Save a pickle of this instance.
summary([yname, xname, title, alpha])	Summarize the proportional hazards regression results.
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q.
t_test_pairwise(term_name[, method, alpha, …])	Perform pairwise t_test with multiple testing corrected p-values.
wald_test(r_matrix[, cov_p, scale, invcov, …])	Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, …])	Compute a sequence of Wald tests for terms over multiple columns.

Properties

baseline_cumulative_hazard	A list (corresponding to the strata) containing the baseline cumulative hazard function evaluated at the event points.
baseline_cumulative_hazard_function	A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function.
bse	Returns the standard errors of the parameter estimates.
llf	Log-likelihood of model
martingale_residuals	The martingale residuals.
pvalues	The two-tailed p values for the t-stats of the params.
schoenfeld_residuals	A matrix containing the Schoenfeld residuals.
score_residuals	A matrix containing the score residuals.
standard_errors	Returns the standard errors of the parameter estimates.
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student’s distribution in inference.
weighted_covariate_averages	The average covariate values within the at-risk set at each event time point, weighted by hazard.