class statsmodels.duration.hazard_regression.PHRegResults(model, params, cov_params, covariance_type='naive')[source]

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

PHregResults inherits from statsmodels.LikelihoodModelResults


See statsmodels.LikelihoodModelResults :


**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



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)
load(fname) load a pickle, (class method)
martingale_residuals() The martingale residuals.
predict([endog, exog, strata, offset, pred_type]) Returns predicted values from the fitted 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
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.
weighted_covariate_averages() The average covariate values within the at-risk set at each event time point, weighted by hazard.



Previous topic


Next topic


This Page