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

Returns:

Attributes

model : class instance

PHreg model instance that called fit.

normalized_cov_params : array

The sampling covariance matrix of the estimates

params : array

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 : array

The standard errors of the fitted parameters.

See also

statsmodels.LikelihoodModelResults

Methods

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.
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...])	Returns 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, **kwd)
llf()
load(fname)	load a pickle, (class method)
martingale_residuals()	The martingale residuals.
normalized_cov_params()
predict([endog, exog, strata, offset, ...])	Returns predicted values from the proportional hazards regression model.
pvalues()
remove_data()	remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])	save a pickle of this instance
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.
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
tvalues()	Return the t-statistic for a given parameter estimate.
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
weighted_covariate_averages()	The average covariate values within the at-risk set at each event time point, weighted by hazard.

Methods

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.
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...])	Returns 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, **kwd)
llf()
load(fname)	load a pickle, (class method)
martingale_residuals()	The martingale residuals.
normalized_cov_params()
predict([endog, exog, strata, offset, ...])	Returns predicted values from the proportional hazards regression model.
pvalues()
remove_data()	remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])	save a pickle of this instance
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.
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
tvalues()	Return the t-statistic for a given parameter estimate.
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
weighted_covariate_averages()	The average covariate values within the at-risk set at each event time point, weighted by hazard.

Attributes

use_t