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

Attributes:

modelclass instance

PHReg model instance that called fit.

normalized_cov_paramsndarray

See specific model class docstring

paramsndarray

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.

bsendarray

The standard errors of the fitted parameters.

Methods

conf_int([alpha])	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, 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, use_t])	Compute a t-test for 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, invcov, use_f, ...])	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

See also

statsmodels.LikelihoodModelResults

Methods

conf_int([alpha])	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, 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, use_t])	Compute a t-test for 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, invcov, use_f, ...])	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