statsmodels.duration.hazard_regression.PHRegResults¶
-
class
statsmodels.duration.hazard_regression.
PHRegResults
(model, params, cov_params, scale=1.0, covariance_type='naive')[source]¶ 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
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.
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
See specific model class docstring
predict
([endog, exog, strata, offset, …])Returns predicted values from the proportional hazards regression model.
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.
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
See specific model class docstring
predict
([endog, exog, strata, offset, …])Returns predicted values from the proportional hazards regression model.
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
A list (corresponding to the strata) containing the baseline cumulative hazard function evaluated at the event points.
A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function.
Returns the standard errors of the parameter estimates.
Log-likelihood of model
The martingale residuals.
The two-tailed p values for the t-stats of the params.
A matrix containing the Schoenfeld residuals.
A matrix containing the score residuals.
Returns the standard errors of the parameter estimates.
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.
The average covariate values within the at-risk set at each event time point, weighted by hazard.